text:
Josef Rieck (* 1911 in Stuttgart; † 1970 in Aulendorf) war ein deutscher Buchhändler und Aktiver im Widerstand gegen den Nationalsozialismus. Er gründete die Buchhandlung Rieck in Aulendorf und war Gründungsmitglied der Gesellschaft Oberschwaben.

Leben
Rieck wuchs in Stuttgart auf und verbrachte einige Zeit als Novize im Kloster Beuron, bevor er sich zum Buchhändler ausbilden ließ. 1938 kam er als bekennender Katholik mit seiner Frau Erika, einer Berliner Kommunistin, ins oberschwäbische Aulendorf, wo er eine eigene Buchhandlung eröffnete. Über diese Buchhandlung hatte Rieck Kontakt zu prominenten Widerstandskämpfern zur Zeit des Nationalsozialismus, weswegen er auch im Zuge der Verhaftung von Mitgliedern der Widerstandsgruppe Weiße Rose kurzzeitig festgenommen wurde. Nach 1945 bediente Rieck mit seiner Buchhandlung vor allem Kunden aus dem Bereich der Theologie und gründete die Gesellschaft Oberschwaben. Rieck wollte mit seinem Wirken stets Geist und Kultur voranbringen, wie ein Auszug aus einem Informationsblatt für seine Kunden aus dem Jahre 1941 verdeutlicht:

Buchhandlung Rieck
Die von Josef Rieck 1938 in Aulendorf eröffnete Buchhandlung Rieck (auch: Rieck'sche Buchhandlung) ist in erster Linie eine Versandbuchhandlung für Theologie und Geisteswissenschaften. Der erste Kunde der neuen Buchhandlung war ihr späterer erster Mitarbeiter: Erwin Glonnegger, der einige Jahre danach als Spieleerfinder berühmt werden sollte. Rieck hatte an sich selbst den Anspruch, in seiner Buchhandlung stets die besten Bücher für seine Kunden vorsortiert zu haben und so die "Käufer durch Angebot und Beratung zu geistigem Qualitätsgefühl zu erziehen".
Mit diesem Anspruch verbunden war eine grundlegende Abneigung gegen Ziele und Methoden des NS-Regimes. Diese kam deutlich zum Ausdruck, als Rieck 500 Exemplare von Alfred von Martins Nietzsche und Burckhardt bestellte, in dem der Autor alles gegen Nietzsche richtete, was man zur damaligen Zeit gegen das Regime eben nicht zu sagen wagen durfte und diese über eine Anzeige in der Frankfurter Zeitung bewarb. Alle 500 Exemplare wurden verkauft, die Besteller waren unter anderem Claus Schenk Graf von Stauffenberg, Werner Bergengruen und die Geschwister Scholl, aber auch Hitlers Chefideologe Alfred Rosenberg. Über den gemeinsamen Bekannten Wilhelm Geyer entstand ein direkter Kontakt zwischen dem Ehepaar Rieck und den Geschwistern Scholl. Regelmäßig wurden Flugblätter der Weißen Rose an die Buchhandlung Rieck geschickt und von dort aus an vertrauenswürdige Personen weiterverbreitet.
Zur Bedeutung der Buchhandlung Rieck während des Nationalsozialismus schrieb Otl Aicher, einer von Riecks Kunden:

Vor allem der Bereich der christlichen und theologischen Literatur war es, der der Buchhandlung Rieck nach dem Zweiten Weltkrieg einen weltumspannenden Kundenstamm sicherte. Der Heilige Stuhl ist der wichtigste Kunde der Aulendorfer, prominenteste Namen in der Kundenkartei sind heute sicherlich Papst Benedikt XVI. und Hans Küng.
Nach Riecks Tod 1970 wurde die Buchhandlung von seiner Frau Erika und später seiner Schwester Eleonore weitergeführt. Seit 1994 ist die langjährige Mitarbeiterin Franziska Rist Geschäftsführerin.

Gesellschaft Oberschwaben
Kurz nach Ende des Zweiten Weltkriegs gründete Rieck die Gesellschaft Oberschwaben mit dem Ziel, ein geistiger Tauschplatz zu werden. Die Gesellschaft sollte "die Pflege heimatlicher Kultur, den Aufbau wissenschaftlicher Institutionen und das fruchtbare Zusammentreffen von Menschen der Wissenschaft und des täglichen Lebens" fördern. Auf der Gründungsversammlung sprach Carlo Schmid zum Thema "Lob Oberschwabens". In wirtschaftlich schwierigen Zeiten standen jedoch nach der langen Not des Krieges materielle Bedürfnisse so sehr im Mittelpunkt, dass Kunst und Kultur zeitweise in den Hintergrund treten mussten, so dass es ab 1949 still um die Gesellschaft Oberschwaben wurde. 1996 entstand eine Nachfolgegesellschaft, die sogenannte Gesellschaft Oberschwaben für Geschichte und Kultur.

Ehrungen
Die Stadt Aulendorf benannte im Jahre 2007 eine Straße nach Josef Rieck.

Literatur
 Erwin Glonnegger: Die Buchhandlung Rieck, ein geistiges Zentrum in Oberschwaben. In: Aulendorf – Im Wandel der Zeit. ISBN 978-3-88294-292-7
 Gesellschaft Oberschwaben für Geschichte und Kultur (Hrsg.): Das große weite Tal der Möglichkeiten. Geist, Politik, Kultur 1945–1949 - Das Projekt Gesellschaft Oberschwaben. ISBN 3-89870-051-8
 Oswald Burger: Josef Rieck, Bücher zum Überleben. In: Im Oberland. 8/1997, S. 9–17.
 Jörg Widmaier, Ein "Weltkloster" für Oberschwaben, Die Verlagsbauten Rolf Gutbrods für den Buchhändler Josef Rieck in Aulendorf, in: Denkmalpflege in Baden-Württemberg, Bd. 48, Nr. 4 (2019), S. 276–282.

Weblinks
 Website der Buchhandlung Rieck

Einzelnachweise

Buchhändler
Person (Widerstand gegen den Nationalsozialismus)
Deutscher
Geboren 1911
Gestorben 1970
Mann


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Report date: July 19 for rookies, July 26 for veterans.
1. Yeah, we all know what the biggest question is.
Chicago has, by far, the most intriguing quarterback training camp battle in the NFL. It's one of the most unique quarterback training camp battles in recent memory. The Bears signed former Buccaneers backup Mike Glennon to a three-year, $45 million contract, a deal that raised plenty of eyebrows. Then they traded up to draft Mitch Trubisky second overall. Even though Glennon has been told, and believes, this is "his year," Trubisky wasn't drafted to sit. From 2006 through last year, 25 of 27 quarterbacks picked in the first round started at least one game as a rookie. If Glennon falters at all, even in training camp, the pressure to move on to Trubisky will begin. Of course, Trubisky is going to be watched closely in camp too. He had just one season as a college starter, so inexperience is an issue. Ideally, the Bears probably would rather Trubisky sit and watch for a year. That just rarely happens. The daily camp updates on the quarterbacks will be fascinating.
2. Can Kevin White turn it around before it's too late?
In 2015, White was the seventh pick of the draft. He was the second receiver taken, after Amari Cooper. The next pick was Vic Beasley, who led the NFL in sacks last season. Three picks after White, 2015 NFL offensive rookie of the year Todd Gurley was selected. White was considered an elite prospect, but you might have forgotten about him by now. NFL teams mentally move on from many players after one lost season to injury, much less two lost seasons. Due to various leg injuries, White has played four NFL games. He has 19 catches for 187 yards and no touchdowns. Chicago would love for White to be healthy and suddenly turn it around, though the history of first-round receivers who have produced as little as White through two seasons is scary. White has a ton to prove in this training camp. First of all, he has to prove he can stay healthy and that the leg injuries haven't sapped his athleticism. Time is running out.
3. Is this the last stand for Ryan Pace and John Fox?
Because the moves to acquire Glennon and Trubisky attracted such strong criticism, the clock is already ticking for general manager Pace. Fox shouldn't feel safe either. In Fox's first two head-coaching jobs, with the Panthers and Broncos, a mediocre first season was followed up by a huge improvement in year two. Fox's first Bears season produced six wins and then in year two … the Bears won three games. It's incredibly rare for a coach to survive three straight double-digit loss seasons. Fox's handling of a tricky quarterback situation, which will start in training camp, is his biggest job. Incorporating some new faces, especially in the secondary, has to come together in camp as well. The Bears better show some improvement this season or there might be a major housecleaning.


Noooo, He is saying he and his government did not do it, Someone else did it for them. TNA claimed there is one, so they might have done it.
Sir, I follow the Buddhist philosophy and as such I am a temporary visit on planet earth. Plant earth too is temporary. Next time you are on a picnic to Kandy tell the guys in yellow fancy dress my opinion on dividing a land mass.
If you still talk about giving priority Buddhism or any other religion and to any community, there will never be true peace or prosperity in Sri Lanka. Ranil, again started murmuring. He should have the guts to firmly say unless there is a new constitution with proper devolution of power to provinces, there will be no future for Sri Lanka!
It's sad we could not solve this Federal word from the days of SWRD ( who initiated this proposal) since 1926 and in 1956 through Banda/Chelva pact. The country would not have lost all its precious citizens over the later years if a new Constitution was evolved under a united island there and then. Hope the country would unite to achieve peacefully a new Constitution at least now for the sake of our future generations' Happy Life!


text:
\section{Introduction}
\label{sec:intro}

\IEEEPARstart{M}{onitoring} and assurance of a good video quality is a very critical task in various applications like video streaming, video surveillance, medical procedures and underwater exploration\cite{wang2002image}\cite{wang2011applications}. Any loss of useful information in these videos, resulting from some kinds of video distortion, may not only affect the visual experience but could also cause fatal consequences like in the fields of video surveillance and medical imaging.
For instance, one of the most common medical procedures, where having a good video quality becomes highly desirable, is the laparoscopic/endoscopic surgery. The latter is a Minimally Invasive Surgery (MIS) which relies on tiny incisions that allow surgeons to enter their surgical instruments inside the body. Among these instruments is the endoscope which is equipped with a camera and is used for the surgical planning and navigation. Hence, a bad video quality can not only cause visual discomfort but can also seriously hamper a surgeon's ability to perform tasks accurately. Such quality may also affect some subsequent processing steps during navigation like registration and segmentation. Moreover, during robotic and image-guided laparoscopic surgeries, there is a need to track instruments in the video. However, the distortions in the captured  video are considered as a main obstacle for achieving accurate track of instruments \cite{sanchez2011laparoscopic} \cite{nwoye2019weakly}. For these reasons, it is imperative to have a quality monitoring and correction system that predicts the video quality, classifies the distortion types in the video and removes them using the appropriate techniques \cite{sdiri2018efficient} \cite{beghdadi2018towards}. Figure \ref{fig:quality_monitor} illustrates the basic idea of such a quality monitoring and correction system.
\begin{figure}[ht]
	\centering
	\includegraphics[width=0.5\textwidth]{Figures/quality_monitor2.png}
	\caption{Basic quality monitoring and correction pipeline.}
	\label{fig:quality_monitor}
	\vspace{-0.2cm}
\end{figure}

Generally, Video Quality Assessment (VQA) has been for more than three decades an active field of research \cite{wolf1991objective,hou2020no}. Despite recent progress, VQA remains a challenging problem that seeks for more efficient solutions. VQA requires integrating more high-level semantic visual information in a well explainable learning process to boost the quality prediction model. Existing VQA methods are generally categorized into three classes, namely Full-Reference (FR), Reduced-Reference (RR) and No-Reference (NR), depending on the availability of the reference video.

However, in many real applications, the reference video  is not available and hence, NR metrics \cite{zhu2014no} are the only plausible solutions as they do not rely on such prior information. Before providing an overview of the VQA methods, let us emphasize here that most of the developed works have been devoted to natural video data \cite{tu2020ugc,chikkerur2011objective,gotz2019no, li2019quality, gotz2021konvid} and only few works have focused on medical data, but most often dedicated to coding and transmission problems. \cite{nouri2010subjective, razaak2014study,leveque2017study}.
\vspace{-0.2cm}
\subsection{Conventional VQA methods}
Research works on VQA have always closely associated it to its image counterpart (i.e Image Quality Assessment (IQA)). A simple and common approach consists first in applying existing IQA methods to each frame and then taking the arithmetic mean of the scores corresponding to all frames to get the final quality score of the video. This is often considered as a kind of a temporal pooling step referred to as average (or arithmetic mean) pooling. In addition, other methods that aim to extend some conventional IQA algorithms for VQA puropose have also been developed. For example, in~\cite{wang2004video}, Wang \textit{et al.} have proposed an extended version of the well known SSIM metric~\cite{wang2004image} to measure the video quality at three levels namely the local region level, the frame level and the sequence level. However, it is not so efficient to address this complex research problem on VQA by simply resorting to simple extension of IQA while neglecting the temporal aspects in the video data and their effect on the global perceptual quality. These spatio-temporal phenomena are closely linked to high-level processes involving learning in the visual cortex. This motivates the use of learning approaches in VQA as well as temporal pooling strategies based on human psycho-visual reasoning like temporal hysteresis \cite{seshadrinathan2011temporal} or fusion-based ensemble pooling~\cite{tu2020comparative}.
To effectively take into account the temporal information in VQA, some metrics based on hand-crafted features exist for natural videos. Indeed, video BLIINDS (V-BLIINDS) \cite{saad2014blind} is a NR VQA metric that uses a combination of motion coherency features and spatial-temporal features based on natural scene statistics. Moreover, MOtion-based Video Integrity Evaluation (MOVIE) \cite{seshadrinathan2009motion} metric and  video intrinsic integrity and distortion evaluation oracle (VIIDEO)~\cite{mittal2015completely} are two other metrics that also use hand-crafted features. MOVIE index is a FR metric that aims to capture the characteristics of Human Visual System (HVS) for video perception by modeling them using separable Gabor filter banks. VIIDEO, on the other hand, is a NR opinion-unaware metric that models statistical naturalness by exploiting space-time statistical regularities.

\vspace{-0.2cm}
\subsection{Neural networks-based VQA methods}
While these hand-crafted features-based VQA methods perform well on the types of distortion and content for which they are designed, they may fail for other kinds of distortion and content. To overcome this drawback, a particular attention has recently been paid to the use of deep neural networks for VQA \cite{hou2020no,liu2018end,ahn2018deep,varga2019no,gotz2019no}. It is important to note here that most of these works only focus on VQA for compressed videos at various severity levels and have not been targeted and tested for other kinds of distortion. For instance, in \cite{liu2018end}, the authors have proposed a single end-to-end optimized  network for compressed videos called V-MEON (Video Multi-task End-to-end Optimized neural Network). Their network is composed of three main blocks used for spatial-temporal feature extraction, codec classification and quality prediction, respectively.
Besides that, some other works make use of neural networks only for feature extraction. For example, in~\cite{ahn2018deep}, the authors have proposed a hybrid approach, called Deep Blind VQA (DeepBVQA),  whereby they have first extracted spatial features from multiple patches for each frame using a pretrained Convolutional Neural Network (CNN). These features are then fused with hand-crafted temporal sharpness variation feature.
Moreover, Varga has proposed in \cite{varga2019no} a different strategy based on temporal pooling of the frame level deep features. Indeed, for extracting deep features from frames, pretrained CNNs (Inception-V3 and Inception-ResNet-V2) are fine-tuned to classify the quality of the frame into one of the five predefined levels. In a similar approach, the authors in \cite{gotz2019no} have used a pretrained  Inception-ResNet-V2 to extract features from multiple layers of this model. Then, instead of temporal pooling, they have tested two strategies. In the first one, the average feature vector is used as an input of a feed-forward neural network. In the second strategy, they have used deep Long Short Term Memory (LSTM) architecture. However, both these methods\cite{varga2019no} \cite{gotz2019no} give good results for one video quality database and do not perform well on the other tested databases.
More recently, Hou \textit{et al.} \cite{hou2020no} have proposed a two-stage network for NR-VQA. Thus, they have first used 12 layers of VGG-Net for extracting frame-based features which are then fed into a 3D CNN. The network is pretrained on ImageNet dataset and tested only on LIVE, CSIQ and VQEG video quality databases consisting mainly of compression and transmission based distortions. Another VQA metric that also employs two deep neural networks, 3D CNN and LSTM, is developed in \cite{you2019deep}.
In addition, the use of Residual Networks (ResNet) for VQA has also been recently investigated. In \cite{li2019quality}, the authors have used pretrained ResNet-50 with global pooling as a spatial feature extractor and called it as content-aware feature extraction. For temporal modeling, they have used Gated Recurrent Unit (GRU) network for learning long-term dependencies and for feature aggregation, followed by subjectively-inspired temporal pooling. They have designed their network specifically for in-the-wild videos and hence did not consider the aspect of distortion classification in their work.
\vspace{-0.2cm}
\subsection{VQA of medical data}
Quality evaluation of medical data is a much more challenging task as compared to that of natural image and video data. This is due to the differences in imaging modalities used, the type of content and the distortions and the sensitivity in terms of the outcomes for the patient. All the works described above have been devoted only to natural video datasets, and as mentioned earlier, very few works have been developed for VQA of medical data. Indeed, most of them resort to classical IQA and VQA metrics already designed for natural data. Moreover, all of these works focus only either on the quality of video in the context of wireless network transmission and compression  \cite{panayides2011atherosclerotic,martini20133,razaak2014study,usman2019suitability} or in case of IQA only on some specific modalities \cite{amirrashedi2021leveraging, fantini2021automatic, das2021diagnostic, zago2018retinal, wu2017fuiqa}.
For example, for quality assessment of compressed laparoscopic videos, Kumcu \textit{et al.} \cite{kumcu2014visual} have used average PSNR, reduced-reference Video Quality Metric (VQM) \cite{pinson2004new} and average of frame-based FR high dynamic range visual difference predictor (HDR-VDP-2) metric \cite{mantiuk2011hdr}. On the other hand, Munzer \textit{et al.} \cite{munzer2014investigation} only provide subjective evaluations for compressed laparoscopic videos. In our previous work \cite{khan2020towards}, we evaluated performance of some of the IQA and VQA methods on laparoscopic videos affected by other types of distortion. These metrics include PSNR, SSIM \cite{wang2004image}, VIF \cite{sheikh2004image}, VIIDEO \cite{mittal2015completely}, BRISQUE \cite{mittal2012no} and NIQE \cite{mittal2012making}. Our results illustrated that none of these metrics showed a good correlation with the subjective scores when all the distortions were considered.
\vspace{-0.2cm}
\subsection{Contributions}
Based on the existing literature, it is clear that there is still a big gap when it comes to video quality assessment of laparoscopic videos, especially for distortions other than compression artifacts. Furthermore, the conventional VQA metrics for natural videos have also shown poor correlations with subjective scores for laparoscopic videos~\cite{khan2020towards}. In this paper, we try to fill the gap in laparoscopic video quality monitoring application by proposing an effective method for quality assessment. The main contributions of this paper are listed below:
\begin{itemize}
	\item First, a new neural network-based approach is developed for NR quality assessment of distorted laparoscopic videos affected by different typical distortions with various severity levels.
	\item Second, the proposed approach is suitable for a dual task of distortion classification and quality prediction based on a combination of ResNet and Fully Connected Neural Network (FCNN). It is important to emphasize here that designing such architecture for the dual-task of frame-level distortion classification and video quality prediction is extremely important for quality monitoring application. To the best of our knowledge, none of the existing deep learning based VQA methods handle these tasks simultaneously in the medical context.
	\item Third, the effective temporal aggregation based on FCNN proposed in this work is a novel approach that has not been considered in any other works on video quality assessment to the best of our knowledge. Most existing works rely either on inefficient conventional temporal pooling methods or on highly computational and difficult to train recurrent neural networks.
	\item Fourth, the ResNet architecture is firstly trained using data with distortion ranking as labels instead of subjective quality scores \cite{itu2008p910} (that are hard to get and need time). This allows us to overcome the problem of lack of large amounts of labeled video data. Note that this idea of using rankings is inspired by a previous work developed for IQA \cite{liu2017rankiqa}, where the authors employ a Siamese network for ranking distortion severity level before fine-tuning its single trained branch to obtain quality scores. However, unlike \cite{liu2017rankiqa}, where only distortion rank is considered, we perform here the training on data with combined labels corresponding to distortion type and rank.
	\item Fifth, the proposed new architecture performs jointly distortions classification/ranking and severity levels identification.
	\item Finally, the previous pre-trained ResNet model is fine-tuned for both the frame-level distortion classification task as well as for the video quality prediction task while adding an FCNN to produce a final video quality score. For instance, the whole architecture is trained using an end-to-end learning approach. Moreover, in order to compute the video quality score from the frame-level ones, different neural network-based temporal pooling stages have also been investigated.
\end{itemize}
It should be noted that a preliminary version of this work has been presented in \cite{khan2020residual}. The previous work has been devoted to distortion classification and ranking and has been developed for laparoscopic images. In this paper, we firstly extend this previous work to predict frame-level quality score. Moreover, we added here a second neural network (FCNN) to make the previous method suitable for video quality prediction. In this respect, two learning approaches are investigated to learn the whole architecture. Finally, extensive experiments are conducted here to show the effectiveness of the proposed method with respect to recent conventional and deep learning based video quality assessment methods.

The remainder of this paper is organized as follows. In Section \ref{sec:icip}, we describe the proposed architecture for frame-level distortion classification and ranking. After that, the extension of the previous architecture to laparoscopic video quality prediction is presented in Section \ref{sec:proposed}. Finally, experimental results are shown and discussed in Section \ref{sec:experiments}, and some conclusions as well as perspectives are drawn in Section \ref{sec:conc}.

\section{Proposed ResNet-based distortion classification and ranking}
\label{sec:icip}
As mentioned in Section \ref{sec:intro}, in the standard two-stage framework based image quality assessment, the feature extraction step plays a crucial role for distortion classification as well as for quality score prediction. In this section, we will focus on the first stage where a ResNet-based solution is proposed to perform jointly distortion classification and ranking. Before describing our approach, let us first describe our developed Laparoscopic Video Quality (LVQ) database recently introduced in \cite{khan2020towards}.
\subsection{Overview of LVQ Database}
\label{dataset_descrip}
LVQ database is composed of 10 reference videos that have been distorted by five typical distortions at four severity levels, yielding a set of 200 distorted videos. For the reference videos, ten clips of 10 seconds were extracted from different videos of the Cholec80 dataset \cite{twinanda2017endonet} which contains a total of 80 videos of cholecystectomy surgeries. To create the LVQ dataset, the reference videos were selected with an attempt to include maximum possible variations of the scene content and temporal information.
The resolution of the videos in the database is 512 $\times$ 288 with a 16:9 aspect ratio and the frame-rate is 25 fps. \\
The five distortions in the LVQ database, which are among the most common ones affecting laparoscopic video, are the smoke (SM), additive white Gaussian noise (WN), uneven illumination (UI), blur due to defocus (DB) and blur due to motion(MB). For each distortion, four different severity levels have been considered. These levels are labeled, from the least to the most distorted one, as hardly visible (HV), just noticeable (JN), very annoying (VA) and extremely annoying (EA). Numerically, they may be represented by numbers from 1 to 4 with 1 being the least severe and 4 being the most severe. Fig.~\ref{fig:diff_dist} illustrates these different distortions on some representative frames. In order to simulate these distortions and generate the distorted videos, appropriate signal acquisition models were applied to each frame of the video. \\
Once all the distorted videos were synthesized, their quality was assessed subjectively by 29 observers. For the subjective testing, the pairwise-comparison protocol
\cite{itu2008p910,qureshi2017towards} was used. More details abo the different distortion models and subjective testing can be found in \cite{khan2020towards}. \\
It is important to note here that, among the medical VQA databases used in the literature, the developed LVQ is the only database to the best of our knowledge, which is made publicly available\footnote{https://github.com/zakopz/LVQ\_Database}. It is also unique in several aspects, such as the diversity of distortions and at different levels, and especially the relatively high participation of expert and non-expert observers.
\begin{figure}[!h]
	\centering
	\subfloat[MB1 \label{fig:mb1}]{\includegraphics[width=0.1\textwidth]{Figures/motion1.png}}\hspace{0.01\textwidth}
	\subfloat[MB2 \label{fig:mb2}]{\includegraphics[width=0.1\textwidth]{Figures/motion2.png}}\hspace{0.01\textwidth}
	\subfloat[MB3 \label{fig:mb3}] {\includegraphics[width=0.1\textwidth]{Figures/motion3.png}}\hspace{0.01\textwidth}
	\subfloat[MB4 \label{fig:mb4}] {\includegraphics[width=0.1\textwidth]{Figures/motion4.png}}\hspace{0.01\textwidth}
	\subfloat[DF1 \label{fig:df1}]{\includegraphics[width=0.1\textwidth]{Figures/defocus1.png}}\hspace{0.01\textwidth}
	\subfloat[DF2 \label{fig:df2}]{\includegraphics[width=0.1\textwidth]{Figures/defocus2.png}}\hspace{0.01\textwidth}
	\subfloat[DF3 \label{fig:df3}]{\includegraphics[width=0.1\textwidth]{Figures/defocus3.png}}\hspace{0.01\textwidth}
	\subfloat[DF4 \label{fig:df4}]{\includegraphics[width=0.1\textwidth]{Figures/defocus4.png}}\hspace{0.01\textwidth}
	\subfloat[UI1\label{fig:ui1}] {\includegraphics[width=0.1\textwidth]{Figures/illum1.png}}\hspace{0.01\textwidth}
	\subfloat[UI2\label{fig:ui2}] {\includegraphics[width=0.1\textwidth]{Figures/illum2.png}}\hspace{0.01\textwidth}
	\subfloat[UI3\label{fig:ui3}]{\includegraphics[width=0.1\textwidth]{Figures/illum3.png}}\hspace{0.01\textwidth}
	\subfloat[UI4\label{fig:ui4}]{\includegraphics[width=0.1\textwidth]{Figures/illum4.png}}\hspace{0.01\textwidth}
	\subfloat[SM1 \label{fig:sm1}]{\includegraphics[width=0.1\textwidth]{Figures/smoke1.png}}\hspace{0.01\textwidth}
	\subfloat[SM2 \label{fig:sm2}]{\includegraphics[width=0.1\textwidth]{Figures/smoke2.png}}\hspace{0.01\textwidth}
	\subfloat[SM3\label{fig:sm3}] {\includegraphics[width=0.1\textwidth]{Figures/smoke3.png}}\hspace{0.01\textwidth}
	\subfloat[SM4\label{fig:sm4}] {\includegraphics[width=0.1\textwidth]{Figures/smoke4.png}}\hspace{0.01\textwidth}
	\subfloat[WN1 \label{fig:noise1}]{\includegraphics[width=0.1\textwidth]{Figures/noise1.png}}\hspace{0.01\textwidth}
	\subfloat[WN2 \label{fig:noise2}]{\includegraphics[width=0.1\textwidth]{Figures/noise2.png}}\hspace{0.01\textwidth}
	\subfloat[WN3 \label{fig:noise3}]{\includegraphics[width=0.1\textwidth]{Figures/noise3.png}}\hspace{0.01\textwidth}
	\subfloat[WN4 \label{fig:noise4}]{\includegraphics[width=0.1\textwidth]{Figures/noise4.png}}
	\caption{\label{fig:diff_dist} Illustration of the different distortions at each severity level for representative frames taken from the LVQ dataset.}
	\vspace{-0.5cm}
\end{figure}

\subsection{Problem formulation}

Once the distorted LVQ database is built, and in order to deal with the lack of labeled data, we propose now to aggregate the tasks of distortion classification and quality ranking as a single multi-label classification problem.
More precisely, let us denote by $X_{d_i,l_j}^{(m)}$ the $m$-th video sequence in the dataset with distortion type $d_i$ and severity level $l_j$, where
\begin{align}
& d_i \in \{DB,MB,WN,SM,UI\} \\
& l_j \in \{HV,JN,VA,EA\}
\end{align}
Such multi-label classification problem can be transformed into a single-label multiclass classification task using one of the Problem Transformation methods \cite{tsoumakas2007multi}. Amongst these methods, we use the Label Powerset approach where each combination of label $(d_i,l_j)$ can be considered as a separate class. This will result in the following set of 20 classes $C$:
\begin{align}
C=\{(d_i,l_j)_{\substack{d_i \in \{DB,MB,WN,SM,UI\} \\l_j \in \{HV,JN,VA,EA\}}}\},
\end{align}
Therefore, the joint distortion classification and ranking tasks can be seen as a single-label multiclass classification problem which will now be solved using a deep learning approach.
\vspace{-0.3cm}
\subsection{ResNet-based solution}
While deep convolutional neural networks have been widely investigated in the literature, it has been shown that they may lead to vanishing gradient problem and drop in accuracy performance due to the use of many stacking layers. To solve this problem, Residual Network (ResNet) has been developed by introducing skip connections \cite{he2016deep}. For instance, the latter has been retained in different image processing tasks such as recognition \cite{he2016deep}, classification \cite{TANSY2020}, etc. For this reason, we propose in this paper to resort to the ResNet architecture to solve our distortion classification and ranking problem.

In this respect, and in order to deal with our video database, we first propose to apply separately the ResNet architecture to all the frames of the different video sequences \cite{khan2020residual}. It is important to note here that using such a frame-based approach allows us to overcome the problem of limited labeled training data for videos. The proposed solution, referred to as Frame-level Distortion Classification ResNet (FDC-ResNet), is shown in Fig. \ref{fig:Resnet_image_class}.

\begin{figure*}[!h]
    \vspace{-0.8cm}
	\centering
	\includegraphics[width = 0.8\textwidth]{Figures/ResNet-LIDC.eps}
	\caption{Proposed Frame-level Distortion Classification Residual Network (FDC-ResNet).}
	\label{fig:Resnet_image_class}
	\vspace{-0.5cm}
\end{figure*}

More precisely, as employed in conventional image classification problem, any given variant of ResNet architectures (i.e. ResNet-18, ResNet-34, ResNet-50, etc) is first applied to the different frames of the distorted video, which will be denoted by $X^{(m,n)}_{d_i,l_j}$ where $m \in \{1,\ldots,M\}$ represents the video sequence index in the dataset and $n \in \{1,\ldots,N\}$ refers to the frame index in that video sequence. Let us also assume that $M \times N$ training distorted frames $(X^{(1,1)}_{d_i,l_j},\ldots,X^{(1,N)}_{d_i,l_j},\ldots,X^{(M,1)}_{d_i,l_j},\ldots,X^{(M,N)}_{d_i,l_j})$ with corresponding labels $(Y^{(1,1)}_{d_i,l_j},\ldots,Y^{(1,N)}_{d_i,l_j},\ldots,Y^{(M,1)}_{d_i,l_j},\ldots,Y^{(M,N)}_{d_i,l_j})$ are available. Thus, by performing the convolution and pooling operations on the training samples in a mini-batch, a feature vector is then generated for each sample. Then, a fully connected layer, with output size equal to the number of classes $C$, is used with a softmax function to generate the following vector of probability scores:
\begin{align}
\mathbf{p}^{(m,n)}=[p_1^{(m,n)},\ldots,p_C^{(m,n)}]^{\top}
\end{align}
where $p_c^{(m,n)}$ is the estimated probability that $X^{(m,n)}_{d_i,l_j}$ belongs to the $c$-th class.

This model is trained by minimizing the cross-entropy function given by
\begin{align}
\mathcal{L}_\mathcal{C}=-\frac{1}{N_\mathcal{B}}\sum_{m,n}\sum_{c=1}^{C} \mathbbm{1}[Y_{d_i,l_j}^{(m,n)}=c]\log(p_c^{(m,n)})
\end{align}
where the indicator function $\mathbbm{1}[Y_{d_i,l_j}^{(m,n)}=c]$ is equal to 1 when the label index $Y_{d_i,l_j}^{(m,n)}$ of the frame $X_{d_i,l_j}^{(m,n)}$ is $c$; otherwise it is equal to 0, and $N_\mathcal{B}$ is the mini-batch size.

Finally, the predicted class $\tilde{c}^{(m,n)}$ of the input frame  $X_{d_i,l_j}^{(m,n)}$ is obtained by selecting the class yielding the maximum probability value:
\begin{align}
\tilde{c}^{(m,n)}=\argmax_{c \in \{1,\ldots,C\}}(p_c^{(m,n)})
\end{align}

Once the FDC-ResNet model is trained, it can be applied on the test video data. Indeed, for each input distorted video $X_{d_i,l_j}^{(m)}$, the trained FDC-ResNet model is applied to all the frames $X_{d_i,l_j}^{(m,n)}$ to generate their respective predicted classes $\tilde{c}^{(m,n)}$, with $n \in \{1,\ldots,N\}$. The trained network is then finally fine-tuned for only distortion classification using $d_i$ as the set of labels. Moreover, the same training can be used for video quality prediction network, proposed in the next section.

\section{Extension to Laparoscopic Video Quality Prediction}
\label{sec:proposed}

\begin{figure*}[!h]
	\centering
	\includegraphics[width = 0.7\textwidth]{Figures/ResNet-LIQP.eps}
	\caption{Proposed  Frame-level Quality Prediction Residual Network (FQP-ResNet).}
	\label{fig:Resnet_image_pred}
	\vspace{-0.5cm}
\end{figure*}

\subsection{Motivation}
Once the feature extraction step is done for distortion classification and ranking, a straightforward approach may consist in resorting to a regression module to map the extracted feature vector to a quality score as generally performed in two-stage framework for blind image quality assessment \cite{moorthy2010two}. However, it would be more interesting to adapt the network model and make it more appropriate for quality prediction task, while taking into account the temporal effects. To this end, and similarly to the previous classification step, we first propose to perform a ResNet-based quality prediction stage on the different frames and then merge the resulting frame quality scores, based on a Fully Connected Neural Network (FCNN), to generate the final quality prediction score of a given video.
\vspace{-0.6cm}
\subsection{Modified ResNet-based solution}
By following the same strategy used in the previous task, and in order to deal with the limited number of labeled training data for videos, we first propose to apply separately our ResNet-based solution on the different frames of the dataset. More precisely, the modified ResNet-based solution, referred to as Frame-level Quality Prediction Residual Network (FQP-ResNet), is shown in
Fig.~\ref{fig:Resnet_image_pred}.

Thus, compared to the previous FDC-ResNet, and after generating the feature vector using the convolution and pooling layers, a fully connected layer of size one neuron in used in the FQP-ResNet to produce the predicted score $\tilde{s}^{(m,n)}$ associated to the $n$-th frame of the $m$-th distorted video. For training this modified ResNet-based prediction model, we propose to start from the pre-trained ResNet model used for classification (as an initialization step) and then fine-tune its different weights to adapt it to the quality prediction task. As a good quality assessment metric should be well correlated with human opinion, the loss function used to optimize the FQP-ResNet weight parameters will be defined based on the Pearson correlation coefficient often employed as a standard criterion to compare image/video quality assessment methods. To this end, we propose to minimize the following loss function:
\begin{align}
\widetilde{\mathcal{L}}_\mathcal{P}=1-\frac{\sum_{m,n}\Big(s^{(m,n)}-\mu_{s}\Big) \Big(\tilde{s}^{(m,n)}-\mu_{\tilde{s}}\Big)}{\sqrt{\sum_{m,n}\Big(s^{(m,n)}-\mu_{s}\Big)^2\sum_{m,n}\Big(\tilde{s}^{(m,n)}-\mu_{\tilde{s}}\Big)^2}}
\end{align}
where $s^{(m,n)}$ is the target subjective score associated to the $n$-th frame of the $m$-th distorted video and, $\mu_{s}$ and $\mu_{\tilde{s}}$ are the means of the target subjective scores and the predicted ones, respectively.

It should be noted here that the subjective score, obtained during the subjective test, are provided for the overall video sequence. Hence, we will assume here that the subjective scores of the different frames are equal to that of the corresponding video sequence. Despite its simplicity, this assumption is in some way reasonable for the following two reasons. First, in the context of laparoscopic surgery, typical video sequences do not show significant variations between the frame contents. Moreover, the temporal effect aspect will be taken into account later when merging the frame quality scores to produce a final score for the overall video.

Once the FQP-ResNet model is described, we will focus now on its application for video quality prediction. The complete architecture, referred to as Video Quality Prediction Network (VQP-Net), is illustrated in Fig.~\ref{fig:Resnet_video_pred}.

\begin{figure*}[!h]
	\centering
	\includegraphics[width=0.85\textwidth]{Figures/resnet-lvqp3.eps}
	\caption{Proposed Video Quality Prediction Network (VQP-Net).}

	\label{fig:Resnet_video_pred}
\end{figure*}

As it can be seen from Fig.~\ref{fig:Resnet_video_pred}, for any input distorted video sequence $X^{(m)}_{d_i,l_j}$, the previous FQP-ResNet is first separately applied to the different frames $X^{(m,n)}_{d_i,l_j}$ resulting in the following subjective quality score vector $\tilde{\mathbf{s}}^{(m)}$:
\begin{align}
\tilde{\mathbf{s}}^{(m)}=[\tilde{s}^{(m,1)},\ldots,\tilde{s}^{(m,N)}]^{\top}
\end{align}
Then, in order to take into account the temporal effect, an additional FCNN is incorporated to combine the different frame quality scores and produce a final video quality score. More precisely, the subjective quality score vector $\tilde{\mathbf{s}}^{(m)}$ is associated to the input layer of this FCNN. Then, $H$ hidden layers are used where their neuron values are computed from the previous ones based on a linear combination (with bias) followed by a nonlinear activation function. Finally, an output layer with a single neuron is employed yielding the computation of the predicted quality score $\hat{s}^{(m)}$ associated to the input distorted video sequence $X^{(m)}_{d_i,l_j}$. \\
For the training of this proposed VQP-Net model, two approaches will now be addressed.

\subsubsection{Transfer learning approach}
One straightforward approach would consist in following a process similar to the transfer learning which is based on the use of a pre-trained model. Thus, the previous pre-trained FQP-ResNet model will be firstly used by the first block of the overall architecture (see Fig.~\ref{fig:Resnet_video_pred}) to generate the frame quality scores. Then, our training strategy will focus only the learning of the FCNN weight parameters. This will be achieved by maximizing the correlation between the target subjective score $s^{(m)}$ and the predicted one $\hat{s}^{(m)}$. Therefore, the FCNN weight parameters are updated by minimizing the following Pearson correlation coefficient based loss function:
\begin{align}
\mathcal{L}_\mathcal{P}=1-\frac{\sum_{m}\Big(s^{(m)}-\mu_{s}\Big) \Big(\hat{s}^{(m)}-\mu_{\hat{s}}\Big)}{\sqrt{\sum_{m}\Big(s^{(m)}-\mu_{s}\Big)^2\sum_{m}\Big(\hat{s}^{(m)}-\mu_{\hat{s}}\Big)^2}}
\label{eq:VQP}
\end{align}

Once the FCNN model is trained, the obtained learned parameters as well as those of the pre-trained ResNet one are employed by the complete VQP-Net architecture for computing the prediction quality score of each input test video sequence.

\subsubsection{End-to-end learning approach}
While the previous approach presents the advantage of reducing the complexity of the training phase, a more interesting approach should consist in resorting to an end-to-end learning approach. More precisely, instead of using the pre-trained FQP-ResNet model (while keeping it fixed) in the first block of the VQP-Net architecture, we propose here to update the weight parameters of the FQP-ResNet model during the training phase. Therefore, the parameters of both the FQP-ResNet model (i.e first block) and the FCNN one (i.e second block) of our VQP-Net architecture are simultaneously optimized by using the same loss function defined in \eqref{eq:VQP}. The final learned parameters of the whole VQP-Net model are then used to predict the quality scores of the test distorted videos.

To show the interest of this end-to-end learning approach compared to the transfer learning one, Fig. \ref{fig:train_comparison} illustrates the loss functions (given by \eqref{eq:VQP}) evaluated on the validation dataset with respect to the number of epochs. Thus, it can be observed the end-to-end training approach outperforms the transfer learning one in terms of loss function values. Moreover, the convergence of the  end-to-end training approach is much faster than that of the transfer learning technique.
\begin{figure}[!h]
	\centering
	\includegraphics[width=0.35\textwidth]{Figures/train_comparison.eps}
	\caption{Loss function evolution with the number of epochs for the transfer learning (TL) and end-to-end (E2E) learning approaches.}
	\label{fig:train_comparison}
	\vspace{-0.5cm}
\end{figure}

\section{Experimental Results}
\label{sec:experiments}
In this section, intensive experiments have been conducted to evaluate the performance of the proposed video distortion classification and quality prediction methods. In the following, we will define the experimental settings used in our simulations, present the comparison methods and finally discuss the obtained results.

\vspace{-0.3cm}
\subsection{Experimental settings}

The proposed ResNet based methods were tested on the LVQ dataset described in Section \ref{dataset_descrip}. For the task related to the distortion classification and ranking (FDC-ResNet), the original training dataset has been extended by creating more distorted videos using the Cholec80 dataset \cite{twinanda2017endonet} and following the methodology explained in \cite{khan2020towards}. Furthermore, a frame-level data augmentation step is also performed by applying random cropping and random horizontal flipping to the original frames. It is important to note here that we did not carry out any subjective test for these new videos since the subjective scores are not used for training the FDC-ResNet. Indeed, the latter only requires the information about the rank and distortion type. After that, for the quality prediction task (FQP-ResNet), the obtained FDC-ResNet model is fine tuned and trained on the original training dataset with available subjective scores. Regarding the VQP-Net task, the FCNN employed in the second block of the overall architecture is implemented using three hidden layers (i.e $H=3$) and the log softmax function as an activation function. Note that 80\% of the original LVQ database is used for training and 20\% for testing. \\
The implementation of the proposed network was done using PyTorch and the network was run on a Windows system with Nvidia Quadro RTX-6000 GPU and 32 GB RAM. For the training of FDC-ResNet, we used the Adam optimizer with a learning rate of 0.01. The learning rate was dynamically reduced if the loss value did not change for two consecutive iterations. For the training of VQP-Net, a lower learning rate of 0.00001 was used with the Adam optimizer.

\subsection{Comparison methods and performance evaluation}
To demonstrate the effectiveness of the proposed video objective quality prediction method, a comparative study with some state-of-the-art VQA metrics is conducted. A first way to evaluate the quality of videos is to consider state-of-the-art IQA metrics designed for images, such as SSIM \cite{wang2004image}, VIF \cite{sheikh2004image}, BRISQUE \cite{mittal2012no} and NIQE \cite{mittal2012making}, apply them frame by frame and use a temporal pooling model to derive VQA measure of the whole video stream. This simple approach was used as the first intuitive solution for estimating the global video quality. For these metrics, we have applied four different temporal pooling techniques for combining the predicted scores from all the frames namely the average pooling, geometric mean pooling, harmonic mean pooling and median pooling. We also considered quality metrics designed specifically for video and which integrate, in one way or another, the temporal aspect according to some pooling models in an explicit or implicit way. These VQA measures can be classified into conventional and deep learning (DL)  based metrics. The conventional metrics used for comparison are V-BLIINDS \cite{saad2014blind}, VIIDEO \cite{mittal2015completely} and TLVQM \cite{korhonen2019two}. For DL based methods, we tested four recent approaches namely Inceptionv3-FT \cite{varga2019no}, VSFA \cite{li2019quality}, CNN-LSTM TLVQM \cite{korhonen2020blind} and CNN-SVR TLVQM \cite{korhonen2020blind}. It should be noted here that these methods have been tested using the source codes provided by the authors except for the Inceptionv3-FT method which is tested using the corrected implementation \cite{gotz2019no}. \\
It is worth mentioning here that all the retained state-of-the-art learning-based methods have also been trained on the new LVQ database for fair comparison.\\
The performance of the different video quality prediction methods is evaluated in terms of various standard criteria measuring the correlation between the predicted scores and the subjective ones. More precisely, we have computed the Pearson Linear Correlation Coefficient (PLCC), Spearman Rank-Order Correlation Coefficient (SROCC) and Kendall Rank-Order Correlation Coefficient (KROCC) after performing five-parameter logistic regression for the predicted scores.
\begin{figure}[!h]
	\centering
	\includegraphics[width=0.45\textwidth]{Figures/conf_mat.png}
	\caption{Confusion matrix for the proposed FDC-ResNet.}
	\label{fig:confusion matrix}
	\vspace{-0.5cm}
\end{figure}
\vspace{-0.2cm}
\subsection{Results and discussion}
\textbf{Distortion classification performance}: First, we have evaluated the classification performance of our proposed architecture and have compared it with some existing classification methods. In this regards, we have selected some of the conventional IQA metrics based on the two-stage network which also perform the classification namely BRISQUE, BIQI and BLIINDS-II. Table \ref{tab:class} shows the accuracy results of these methods. Thus, it can be noticed that our proposed method outperforms the conventional ones and yields a classification accuracy of 97.8\%. Furthermore, Fig. \ref{fig:confusion matrix} illustrates well, through the confusion matrix, the superiority of the proposed distortion classification method in terms of prediction error and more particularly in the case of the two most common distortions, i.e. noise and and uneven illumination.

\begin{table}[htbp]
	\caption{Comparison of distortion classification accuracy.}
	\label{tab:class}
	\begin{center}
		\begin{tabular}[width=0.4\textwidth]{|c|c|}
		\hline
		\textbf{Method} & \textbf{Classification Accuracy} \\
		\hline
		\textbf{FDC-ResNet} & 97.8\%\\
		\hline
		\textbf{BRISQUE} & 94.2\%\\
		\hline
		\textbf{BIQI} & 95.0\%\\
		\hline
		\textbf{BLIINDS-II} & 95.6\% \\
		\hline
		\end{tabular}
	\end{center}
	\vspace{-0.3cm}
\end{table}

\textbf{Video quality prediction performance}: Table~\ref{tab:state_of_the_art} shows the results of our proposed methods as well as the state-of-the-art ones for the LVQ database in terms of PLCC, SROCC and KROCC. Note that the category of metric to which belongs each metric (conventional or deep learning (DL) one) as well as its type (full-reference (FR) or no-reference (NR) one) are also provided.
\begin{table*}[htbp]
	\caption{PLCC, SROCC and KROCC for the scores of the different video quality prediction methods.}
	\begin{center}
		\renewcommand\arraystretch{1.2}
		\begin{tabular}[width=0.4\textwidth]{|l|l|l|l|l|l|l|l|l|l|l|l|l|}
			\hline
			\rule[-1ex]{0pt}{2.5ex} \textbf{Metric} & \textbf{Category} & \textbf{Type}  & \textbf{Temporal Model}
			&  \textbf{PLCC} &  \textbf{SROCC} &  \textbf{KROCC} \\
			\hline
			\rule[-1ex]{0pt}{3.5ex} \multirow{4}*{\textbf{PSNR}}&\multirow{4}*{Conventional}&\multirow{4}*{FR-IQA}&Average Pooling&0.6973&0.6996&0.5030 \\
			\cline{4-7} &  &  & Geometric Mean Pooling & 0.6900 & 0.6949&0.4965 \\
			\cline{4-7} &  &  & Harmonic Mean Pooling & 0.6778 & 0.6829&0.4875 \\
			\cline{4-7} &  &  & Median Pooling & 0.7131 & 0.7097&0.5133 \\
			\hline
			\rule[-1ex]{0pt}{3.5ex} \multirow{4}*{\textbf{SSIM \cite{wang2004image}}}& \multirow{4}*{Conventional}& \multirow{4}*{FR-IQA}& Average Pooling&0.6123&0.5914&0.4187 \\
			\cline{4-7} &  &  & Geometric Mean Pooling & 0.6034 & 0.5812&0.4123 \\
			\cline{4-7} &  &  & Harmonic Mean Pooling & 0.5902 & 0.5563 &0.3980\\
			\cline{4-7} &  &  & Median Pooling & 0.6157 & 0.5945 &0.4183\\
			\hline
			\rule[-1ex]{0pt}{3.5ex} \multirow{4}*{\textbf{VIF \cite{sheikh2004image}}}&\multirow{4}*{Conventional}&\multirow{4}*{FR-IQA}&Average Pooling&0.6267&0.6228&0.4537 \\
			\cline{4-7} &  &  & Geometric Mean Pooling & 0.6158 & 0.6307 & 0.4503 \\
			\cline{4-7} &  &  & Harmonic Mean Pooling & 0.5834 & 0.6081 & 0.4397\\
			\cline{4-7} &  &  & Median Pooling & 0.6211 & 0.6192 & 0.4504\\
			\hline
			\rule[-1ex]{0pt}{3.5ex} \multirow{4}*{\textbf{BRISQUE \cite{mittal2012no}}}&\multirow{4}*{Conventional} &\multirow{4}*{NR-IQA}&Average Pooling&  0.4593 &  0.4304&0.3108 \\
			\cline{4-7} &  &  & Geometric Mean Pooling & 0.4609 & 0.4300&0.3114 \\
			\cline{4-7} &  &  & Harmonic Mean Pooling & 0.4722 & 0.4360&0.3199 \\
			\cline{4-7} &  &  & Median Pooling & 0.4639 & 0.4310&0.3119 \\
			\hline
			\rule[-1ex]{0pt}{3.5ex} \multirow{4}*{\textbf{NIQE \cite{mittal2012making}}}&\multirow{4}*{Conventional}& \multirow{4}*{NR-IQA}&Average Pooling&0.4242 &   0.3731 &0.3555\\
			\cline{4-7} &  &  & Geometric Mean Pooling & 0.4535 & 0.4958&0.3594 \\
			\cline{4-7} &  &  & Harmonic Mean Pooling & 0.4402 & 0.4856&0.3641 \\
			\cline{4-7} &  &  & Median Pooling & 0.4583 & 0.4994&0.3633 \\
			\hline
			\hline
			\rule[-1ex]{0pt}{3.5ex} \textbf{VIIDEO \cite{mittal2015completely}}&Conventional&NR-VQA&Temporal Features&0.3842&0.3416&0.2313
			\\
			\hline
			\rule[-1ex]{0pt}{3.5ex} \textbf{V-BLIINDS \cite{saad2014blind}}&Conventional&NR-VQA&Temporal Features&0.8328&0.8242&0.6317
			\\
			\hline
			\rule[-1ex]{0pt}{3.5ex} \textbf{TLVQM \cite{korhonen2019two}}&Conventional&NR-VQA& Average Pooling&0.7681&0.6892&0.5132
			\\
			\hline
			\hline
			\rule[-1ex]{0pt}{3.5ex} \textbf{CNN-SVR TLVQM \cite{korhonen2020blind}}&DL&NR-VQA&SVR&0.5826&0.5888&0.4088
			\\
			\hline
			\rule[-1ex]{0pt}{3.5ex} \textbf{CNN-LSTM TLVQM \cite{korhonen2020blind}}&DL&NR-VQA&LSTM&0.8006&0.7829&0.5828
			\\
			\hline
			\rule[-1ex]{0pt}{3.5ex} \textbf{Inceptionv3-FT \cite{varga2019no} \cite{gotz2020comment}}&DL&NR-VQA& Average Pooling&0.8550&0.7978&0.6202
			\\
			\hline
			\rule[-1ex]{0pt}{3.5ex} \textbf{VSFA \cite{li2019quality}}&DL&NR-VQA& GRU + Subjectively-inspired&0.9647&0.9247&0.7629
			\\
			\hline
			\rule[-1ex]{0pt}{3.5ex} \textbf{Proposed VQP-Net (TL)}&DL&NR-VQA&Fully-connected Network&0.8992&0.8434&0.6494
			\\
			\hline
			\rule[-1ex]{0pt}{3.5ex} \textbf{Proposed VQP-Net (E2E)}&DL&NR-VQA&Fully-connected Network&\textbf{0.9899}&\textbf{0.9388}&\textbf{0.7739}
			\\
			\hline
		\end{tabular}
		\label{tab:state_of_the_art}
	\end{center}
	\vspace{-0.5cm}
\end{table*}
From the table, it can be firstly observed that the IQA based methods perform poorly  in terms of PLCC, SROCC and KROCC, even when using temporal pooling strategies other than the average pooling one. Among these IQA-based video quality assessment methods, the highest correlation values are obtained using the PSNR metric with median pooling. Secondly, regarding the NR-VQA methods, except for the VIIDEO metric, TLVQM and V-BLIINDS ones outperform all the conventional IQA-based video quality metrics. Indeed, V-BLIINDS leads to the best results compared to the other conventional IQA-based metrics and VQA ones. The obtained PLCC, SROCC and KROCC values are 0.8328, 0.8242 and 0.6317, respectively. Finally, concerning the DL based VQA methods, one can observe that the performance of the CNN-TLVQP method depends on the temporal pooling strategy. Indeed, while the Support Vector Regression (SVR) based temporal pooling stage is less performant compared to some conventional methods, the Long Short-Term Memory (LSTM) based temporal stage improves significantly the results yielding a gain of around 0.2 in all the correlation values. Further improvements are achieved by the Inception-v3 method, and more specifically, the VSFA method. For the proposed VQP-Net method, it can be firstly noticed that its first version based on the transfer learning (TL), outperforms the state-of-the-art deep learning ones except the VSFA method. Most importantly, the end-to-end learned version (E2E) allows us to improve the VSFA method while achieving a gain of around 2.5 \% in terms of PLCC and 1.5~\% in terms of SROCC and KROCC.

In order to have a better idea of the prediction accuracy for these VQA methods, Figure \ref{fig:mos_plots} shows the scatter plots of subjective scores (MOS) against the normalized predicted scores. All the plots are obtained by using the scores for the test data.
\begin{figure}[!http]
	\centering
	\subfloat[PSNR \label{fig:psnr}]{\includegraphics[width=0.15\textwidth]{Figures/PSNR_test_scatter_norm.eps}}\hspace{0.01\textwidth}
	\subfloat[SSIM \label{fig:ssim}] {\includegraphics[width=0.15\textwidth]{Figures/SSIM_test_scatter_norm.eps}}\hspace{0.01\textwidth}
	\subfloat[VIF \label{fig:vif}]{\includegraphics[width=0.15\textwidth]{Figures/VIF_test_scatter_norm.eps}}\hspace{0.01\textwidth}
	\subfloat[BRISQUE \label{fig:brisque}]{\includegraphics[width=0.15\textwidth]{Figures/BRISQUE_test_scatter_norm.eps}}\hspace{0.01\textwidth}
	\subfloat[NIQE\label{fig:niqe}] {\includegraphics[width=0.15\textwidth]{Figures/NIQE_test_scatter_norm.eps}}\hspace{0.01\textwidth}
	\subfloat[VIIDEO\label{fig:viideo}]{\includegraphics[width=0.15\textwidth]{Figures/VIIDEO_test_scatter_norm.eps}}\hspace{0.01\textwidth}
	\subfloat[V-BLIINDS \label{fig:vbliinds}]{\includegraphics[width=0.15\textwidth]{Figures/vbliinds_test_scatter_norm.eps}}\hspace{0.01\textwidth}
	\subfloat[Inceptionv3-FT\label{fig:inceptionv3-ft}] {\includegraphics[width=0.15\textwidth]{Figures/Inception_scatter_norm.eps}}\hspace{0.01\textwidth}
	\subfloat[VSFA\label{fig:vsfa}] {\includegraphics[width=0.15\textwidth]{Figures/vsfa_scatter_norm.eps}}\hspace{0.01\textwidth}
	\subfloat[TLVQM\label{fig:tlvqm}] {\includegraphics[width=0.15\textwidth]{Figures/tlvqm_scatter_norm.eps}}\hspace{0.01\textwidth}
	\subfloat[CNN-LSTM TLVQM\label{fig:tlvqm_lstm}] {\includegraphics[width=0.15\textwidth]{Figures/tlvqm_lstm_scatter_norm.eps}}\hspace{0.01\textwidth}
	\subfloat[CNN-SVR TLVQM \label{fig:tlvqm_svr}] {\includegraphics[width=0.15\textwidth]{Figures/tlvqm_svr_scatter_norm.eps}}\hspace{0.01\textwidth}
	\subfloat[Proposed VQP-Net (TL) \label{fig:proposed}]{\includegraphics[width=0.15\textwidth]{Figures/proposed_scatter_norm.eps}}\hspace{0.01\textwidth}
	\subfloat[Proposed VQP-Net (E2E) \label{fig:proposed_e2e}]{\includegraphics[width=0.15\textwidth]{Figures/proposed_e2e_scatter_norm.eps}}
	\caption{\label{fig:mos_plots} Subjective (MOS) vs predicted score plots for different VQA metrics.}
	\vspace{-0.5cm}
\end{figure}

Ideally, for a good VQA method, the scatter plot should show good linearity, tight clustering and a relatively uniform density along both axes. From the figure, we can see that with IQA metrics and VIIDEO (Figure \ref{fig:psnr} - \ref{fig:viideo}), there is no clear relationship between the two data and they are widely spread throughout. If the points on each plot are closer to the fitted lines, it implies a higher consistency between the subjective and predicted scores. Hence, we can clearly see the performance of the different metrics based on the spread of the data points around the straight line. For instance, for TLVQM based methods (Figure \ref{fig:tlvqm} - \ref{fig:tlvqm_svr}), the data is more spread as compared to the other methods, especially for the CNN-SVR TLVQM metric where the predicted scores are significantly biased towards higher values. On the other hand, for V-BLIINDS and Inceptionv3-FT (Figure \ref{fig:vbliinds} - \ref{fig:inceptionv3-ft}), we can observe that data points are closer and more uniformly distributed as compared to the previously mentioned methods. Similar behavior is also observed with our proposed VQP-Net (TL) method with transfer learning approach. However, with VSFA method (Figure \ref{fig:vsfa}), we see even much better clustering than our proposed transfer learning based approach, even though some of the data points are still away from the fitted line. Undoubtedly, from among all the plots, our proposed VQP-Net (E2E) with the end-to-end learning approach gives the best linearity, clustering and uniform distribution along the two axes as shown in Figure \ref{fig:proposed_e2e}. This further consolidates the observations made based on the correlation coefficient values.

\textbf{Comparison with different temporal pooling approaches}:
In order to show the importance of the additional FCNN model to combine the different frame quality scores, we have performed an ablation study whereby this temporal pooling stage is achieved using other approaches based on neural networks as well as conventional operations. In this respect, we have chosen three different networks which have been considered in previous deep learning based VQA methods  \cite{li2019quality,korhonen2020blind}. These networks are Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN). For fair comparison, the overall architecture composed of ResNet followed by the chosen network is trained in an end-to-end manner.
Regarding the conventional temporal pooling methods, we have used arithmetic mean, geometric mean, harmonic mean and median pooling approaches. Note that the latter are simply applied to the quality scores $\tilde{s}^{(m,n)}$ obtained with each frame using the FQP-ResNet model.\\
The results of these different temporal pooling approaches are shown in Table \ref{tab:temp_pooling}. Thus, in case of conventional temporal pooling methods, it can be firstly noticed that both arithmetic mean pooling and median pooling give good results which are close to those obtained using an FCNN with a transfer learning strategy. However, the results of these simple temporal pooling approaches are much less performant than the end-to-end learning version of the FCNN-based approach. Moreover, it can be also observed that the retained FCNN model outperforms significantly the other neural network ones.

\begin{table}[htbp]
	\caption{Comparison of different temporal pooling methods for combining the frame quality scores.}
	\begin{center}
		\resizebox{0.5\textwidth}{!}{\begin{tabular}[width=0.4\textwidth]{|l|c|c|c|}
			\hline
			\rule[-1ex]{0pt}{2.5ex} \textbf{Methods}
			&  \textbf{PLCC} &  \textbf{SROCC}  &  \textbf{KROCC}  \\
			\hline
			\rule[-1ex]{0pt}{2.5ex} \textbf{Proposed FCNN (E2E)}&\textbf{0.9899}&\textbf{0.9388} & \textbf{0.7739}\\
			\hline
			\rule[-1ex]{0pt}{2.5ex} \textbf{Proposed FCNN (TL)}&0.8992&0.8434 & 0.6494\\
			\hline
			\rule[-1ex]{0pt}{2.5ex} \textbf{RNN (E2E)}&0.7768&0.7039&0.5276 \\
			\hline
			\rule[-1ex]{0pt}{2.5ex} \textbf{GRU (E2E)}&0.6938&0.6331 & 0.4550 \\
			\hline
			\rule[-1ex]{0pt}{2.5ex} \textbf{LSTM (E2E)}&0.5434&0.4880 & 0.3461 \\
			\hline
			\hline
			\rule[-1ex]{0pt}{2.5ex} \textbf{Arithmetic Mean Pooling}&0.9019  &  0.8459 & 0.6494
			\\
			\hline
			\rule[-1ex]{0pt}{2.5ex} \textbf{Geometric Mean Pooling}&0.7728  &  0.7112 & 0.4939
			\\
			\hline
			\rule[-1ex]{0pt}{2.5ex} \textbf{Harmonic Mean Pooling}&0.7803  &  0.7190 & 0.5276
			\\
			\hline
			\rule[-1ex]{0pt}{2.5ex} \textbf{Median Pooling}&0.9033  &  0.8456 & 0.6520
			\\
			\hline
		\end{tabular}}
		\label{tab:temp_pooling}
	\end{center}
	\vspace{-0.7cm}
\end{table}

\section{Conclusion and Perspectives}
\label{sec:conc}
In this work we addressed a rather complex problem that concerns both the identification and classification of distortions and the evaluation of video sequence quality in the context of video-guided surgery. We have then proposed neural networks based approaches for distortion classification and quality prediction in laparoscopic video. While a residual network architecture is employed for the first task, the second one is achieved by adding a fully connected network to combine the quality scores obtained on the different frames of the video sequence. The resulting architecture for the second task is trained using transfer learning and end-to-end learning approaches. The obtained results confirm the benefits of the proposed approaches (i.e. the
proposed end-to-end learning approach) compared to the recent state-of-the-art methods.
In future work, it would be interesting to enrich the database with other videos of different organs and surgical scenarios. It would be also interesting to investigate new perceptual loss functions for training the overall architecture.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


"A pair of arms abruptly slid over my shoulders from behind and hugged my neck, and I yelped in sudden shock until the familiar laugh next to my right ear had me falling completely still instead of fighting against the unexpected hold. I couldn't believe that I had been so deep in thought that I hadn't even heard him step up behind me!
My cheeks flaming in embarrassment, they got even worse when Sethian slipped into the pool beside me, and I was treated to a full-frontal sideshow..."


text:
St. David's Episcopal Church ist der Name folgender im NRHP gelisteten Objekte:

 St. David's Episcopal Church (Rayville, Louisiana), NRHP-ID 07001006
 St. David's Episcopal Church (Austin, Texas), NRHP-ID 78002994

redpajama_set_name: RedPajamaWikipedia


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text:
Hekmatyar Warns of 'Dire Consequences' If IEC Ignores His Demands
(Last Updated On: November 26, 2019)
Former Jihadi Leader and Presidential Candidate Gulbuddin Hekmatyar on Tuesday warned that no one is allowed to announce the preliminary results of election until all non-biometric votes are invalidated.
Speaking at a press conference in Kabul, Hekmatyar emphasized that 1.2 million fraudulent votes and 300,000 quarantined votes must be excluded from the partial recount process.
"There must be a full recount process in the presence of observers. [A] partial vote recount is not acceptable, especially when it is directed by a specific team," Hekmatyar said.
In addition, he called for holding a reelection in those polling sites where technical problems existed on Election Day.
The Leader of Peace and Islamic Justice campaign warned of dire consequences if the Election Commission refuses to accept their demands.
"If the Election Commission ignores our legitimate demands, and rely on the ruling team or support them. It will have a dire consequences for the fraudsters," he added.
Hekmatyar also called for the trial of the German Dermalog Company, saying it has failed to fulfill its commitments. He said the company must reimburse all the money it has received.
The Former Jihadi leader asked the international community to stay impartial when it comes to election affairs.
By Hesamuddin Hesam
Related Topics:Afghan ElectionsAfghanistanAriana NewsBiometricElections 2019FraudGulbuddin HekmatyarIECPreliminary ResultsRecount ProcessTransparencyvote
Watchdog Questions AGO Progress on Cases Related to Former Afghani Ministers
Taliban Confirms 'Successful' Exchange of Prisoners
IEA assures the public their taxes will be spent wisely
Pakistan envoy says Islamabad will continue to support Afghan students
The Ministry of Finance (MoF) of the Islamic Emirate of Afghanistan said on Sunday a plan outlining the collection process of Usher (Islamic tax on certain harvests) and Zakat (Islamic tax on personal income) has been finalized and will be submitted to the council of ministers soon.
Officials said that after the approval of the plan a special board will be established to collect the Usher and Zakat from Afghan citizens.
"Two things are very important to us; first we want to collect it (Usher and Zakat) via a digital system, and second we want to spend it via a regular system based on Sharia laws," said Ahmad Wali Haqmal, spokesman for the MoF.
Some clerics meanwhile welcomed the IEA's plan, saying that Zakat and Usher money should be distributed to people who deserve it.
"When the officials collect Zakat from traders, it should reach deserving people, and the process should be transparent. Second, it should be done in coordination with the Ministry of Commerce and Chamber of Commerce, which registers traders' properties," said Amanullah Ahmadi, a cleric.
The move however was met by mixed reactions among the public.
One Kabul resident, Ahmad Wais Akbari said: "People's financial situation should be understood, employment opportunities should be created, salaries should be paid, in this case, everyone is ready, but the current situation is problematic," said Akbari.
"We accept this but employment opportunities should be created for people, there is a lot of problems," said Abdul Ghafar, a resident of Balkh.
"We demand the Islamic Emirate to not collect Usher from poor farmers," said Naqibullah, a farmer in Takhar.
This comes as many investors fled the country after the mid-August takeover by the IEA and Afghans who stayed behind are dealing with a severe economic crisis.
China's ambassador to Afghanistan Wang Yu criticized the United States for seeking revenge on Afghans for its defeat by imposing economic sanctions, and said that the US was using the issues of an inclusive government, human rights, women's rights and freedom of expression as pretexts in Afghanistan.
In a meeting with Acting Interior Minister Sirajuddin Haqqani, on Sunday, the envoy said that China wants to work with Afghans to help them.
During the meeting Wang called on the Islamic Emirate of Afghanistan to ensure that Afghan soil will not be used against any country, the interior ministry said.
Meanwhile, the Afghan interior ministry officials thanked China for its humanitarian assistance and called on China to continue its assistance to the Afghan people during this difficult time.
Islamic Emirate of Afghanistan (IEA) officials said Sunday that five million Afghans have become addicted to drugs in the past 20 years but that these addicts will be provided with treatment.
Speaking at an event in Kabul on Saturday, Abdul Salam Hanafi, deputy prime minister of Afghanistan said: "We have seen that nothing was done for Afghanistan during the occupation period, however, our youths are addicts and social problems increased."
Meanwhile, the minister of information and culture of IEA, Khairullah Khairkhwa said that in addition to sanctions, some foreign countries launched propaganda campaigns against the IEA after their defeat.
"Experience has shown that countries which faced sanctions, built their own countries. We will solve the current problems," said Khairkhwa.
IEA officials also welcomed humanitarian assistance being provided by the international community but reiterated that they would not accept conditions based assistance.
Officials also called on the people to accelerate their efforts to improve the current economic, social and health crisis
Saar1 hour ago

redpajama_set_name: RedPajamaCommonCrawl


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Being Digitalz, in its essence, is made of passionate search marketers, business brains, innovative designers, web developers, and advertising junkies who have been on both sides of the advertising world. With our digital proficiency in domains of Website Development, SEO (Inbound marketing), SEM (Google AdWords / PPC), Email marketing, ORM, and Social Media Advertising with Facebook, Twitter, and YouTube advertising, you can rest assured of your brands reaching out to the right customers across the right touchpoints.
We are looking for an experienced Search Engine Executive, who shares our passion and drive, to join our team and open the doors to new fantastic opportunities that will allow us to take the business to the next level.
As a Search Engine Marketing Executive at Being Digitalz, we expect you to undertake all roles and responsibilities set out in the job description. The company may change your responsibilities from time to time, you would be informed prior to the changes.


For some who grow up in rural Arkansas there's limited options: you can either be a meth dealer or a cop chasing meth dealers. Set in a close-knit community of Faulkner County, near Conway, native sons of the region Craig and Brent Renaud have crafted a harrowing look at a community that has no easy answers with Meth Storm. Following two families impacted by the drug trade, on one side is Johnny, a sympathetic DEA agent who has locked up his fair share of classmates, children of classmates, and friends for their involvement in the meth trade. What was once a network of mom-and-pop operations cooking meth locally had been put to an end thanks to laws that require you to show an ID when purchasing Sudafed. The addicts in the region now present an opportunity for the Mexican cartels to peddle high-grade crystallized meth, called Ice, into these poor communities, turning addicts into dealers.
On the other side is Veronica, the matriarch of a long-time family of users. At 45, she looks easily twenty years older. In and out of the system herself, moving from home to home, she's supported by two straight-edge daughters, while her sons become her drug buddies, searching for the next vein to tap. These passages are excruciating as is the emotional toll of son Teddy's multiple incarcerations. As the film begins he's 26, sentenced to years in prison away from his daughters. He eventually cleans up, writing letters praising the drug-free lifestyle to his mother who knows better. There is very little hope here.
As Johnny–also a small town mayor–explains, thriving downtown economies used to exist until the introduction of Wal Mart's always low prices undercut the small guy. I'm sure there's a little more to it than that, but the film offers up no easy solutions for making rural Arkansas great again. Johnny, however, laments decriminalization and federal budget cuts, telling the filmmakers directly as his agents are buying meth at an interstate motel. The DEA can only afford to buy as much as they used to.
Directed with intimate access to both families as a larger proxy for the issue, Meth Storm quickly evolves from an opening that feels like an episode of Cops into a hard-hitting issues documentary with an unprecedented view into this specific kind of hell. The Renauds don't shy away from the dangers of both sides, offering a frank look at the early day lives of Veronica and family as they struggle to survive, making the most of their chards of ice and attending church to find some salvation. The faith community has made in-roads operating a recovery camp, but there's only so much that can be done here.
Exploring a community that has yet to see the heroin and fentanyl wave hit, the Renauds have made an essential documentary. While Johnny has seen some success for those he's arrested that this can be a turning point for them, there's little support and even less opportunity in these communities. Meth Storm may very well be the most heartbreaking exploration of Americana you'll see all year.
Meth Storm screened at DOC NYC and will premiere on HBO on November 27.


Re: Avid on a 4k-screen with 4k-resolution impossible?
Hi Sorry to bother you. It seems that after longtime I'm having the same problem. Can I ask you if you were able to solve it and how? Thank you.
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The "Minpaku" was founded in 1974 as an inter-university research institute and opened to the public in 1977.
10 a.m.-5 p.m. (last admission 4:30 p.m.). Closed Wed. (except national holidays, in which case closed Thu.), New Year's holidays (Dec. 28-Jan.4).


text:
10 Medics Quarantined After Patient Dies
By Imran Otieno on 18 April 2020 - 8:15 pm
Medical practitioners at a Coronavirus isolation and treatment facility in Mbagathi District Hospital on Friday, March 6, 2020.
10 health workers in Bomet were quarantined after coming into contact with a woman who died while receiving treatment, having exhibited Covid-19 symptoms.
The woman is said to be a 56-year-old teacher of a local school and died at Longisa County Referral Hospital on Friday night April 17.
The woman had arrived from Uganda recently and was advised to quarantine but flaunted the directive and went to Kericho before spending a night in Kisii.
She arrived in the hospital claiming to have a heart condition but had been coughing, sweating and her body temperature was at 45°C.
File image of a Covid-19 testing kit.
In an interview with Nation, Public Health CEC Joseph Sitonik called for calm as the patient is yet to be confirmed positive.
"There is no cause for alarm over the issue as it is being treated as a suspected case due to the nature of the symptoms exhibited," he stated.
Sitonik added that a surveillance team had been deployed to the patient's rural home to trace contacts that she interacted with.
The woman is said to have been admitted at Longisa County Hospital ICU and had been suffering from diabetes and high blood pressure.
Her samples have been collected and taken to the Kenya Medical Research Institute (KEMRI) in Kisumu County for testing.
The Public Health CEC said the body was placed in a bag and taken to the mortuary and noted that a comprehensive statement would be issued once results are out.
Police in the area have also stepped up their search for 24 people alleged to have sneaked into the county from Nairobi.
As of Saturday, April 18, the country had a total of 262 cases with 12 fatalities and 60 recoveries.
A medical practitioner has her name written on the protective gear at a Coronavirus isolation and treatment facility in Mbagathi District Hospital on Friday, March 6, 2020.


Studio A at New York's Electric Lady, one of the most fabled recording studios.
Electric Lady is the house that Hendrix built. Through a discreet doorway in New York's Greenwich Village, you slip into what seems like a psychedelic spaceship – a musician's Shangri-La.
On any given day, in its four studios, you could catch Lucius laying down a track – or The Black Keys' Dan Auerbach at work – or in Studio A, Roger Waters, of Pink Floyd.
"I love this studio," he said. "I love the old hippie-dippy thing; just going up and down the corridors is kind of an experience. And that room is a beautiful room. It's got a proper piano in it. So it encourages you to still believe you're a musician!"
Electric Lady was the first recording studio designed and built for an artist. John Storyk was a young architect when he got a call in 1969: "'Do you wanna do a club for Jimi Hendrix?' Imagine, I'm 22!"
The secret to the sound, Storyk says, came from the unusually-shaped ceiling: "And I think that's the magic in the room. I now know why the ceiling is working. At 22 it was a guess."
Hendrix unveiled his studio with an opening night party on August 26, 1970. Patti Smith was there: "I talked to him. He told me he was on his way to London. He told me all his hopes and dreams. I still harbor them."
The next day, he left for England, where three weeks later Jimi Hendrix would die of a drug overdose. He was 27.
But his studio did not fall silent. In the seventies, Stevie Wonder would record three albums there (including "Talking Book"). Led Zeppelin made parts of "Physical Graffiti." David Bowie recorded "Young Americans."
Though Hendrix barely got to use his dream studio, other musicians were drawn to its magic.
And when Questlove produced D'Angelo's landmark "Voodoo" album at "Jimi's house" in the late '90s, they became convinced that Jimi the house cat, was in fact Hendrix himself.
"There was a point where there was this like shrieking feedback noise was happening," Questlove said. "None of the engineers could find the source of it. The cat jumped on top of the speakers, walked over and pressed a button, just muted that noise. That's Jimmy Hendrix!" he laughed.
But as the century turned and the internet began to erode the music business, Electric Lady lost its allure.
"In fact, there was a 10-month period when there was not a single session here," said Lee Foster. He'd started as an intern at the studio in 2002: "And I'm thinking, you know, 'This is gonna be Shangri-La, this is gonna be amazing.' And I walked in and the very first thing that I remember about this place is that a ceiling tile had broken through, and someone had taken a white pizza box and shoved it in its place as a replacement."
Foster would work his way up to become general manager, determined not to let the Hendrix dream die.
Correspondent Anthony Mason asked, "The history of this place meant something to you?"
"Sure," said Foster. "And it felt like something worthy of thought and protection. Do something."
"I worked really, really hard."
Foster spent nights in the studio to fix it up, and offered free sessions to artists he admired, like Ryan Adams. Early one morning in 2006, it started to pay off. "And he called at 5 a.m. and he said, 'Hey Lee, it's Ryan!'"
Adams remembered the call: "And I was like, 'Hey, man, remember you said I could have a free session?'"
"And I was like, 'No, like, I mean now! I really need a place now, I have this song idea.'"
"He was literally outside the building!" Foster recalled. "And I said, 'Ryan, it's 5:00 a.m., but you know, I'll come now, if that's what you're asking me.'"
It was, Foster said, a "massive" moment for him.
"It was like a Noah's Ark moment," Foster said. "I wanna get everybody here and get 'em safe and we can all travel together."
"That's a great image," said Mason.
"I think that's what Jimi wanted. Because if you look around at all the paintings and you're in this vehicle that he sort of had in his head, and it's like we're all traveling through space or whatever, it's kinda beautiful, you know what I mean? Let's go somewhere else. Let's all go together."
Story produced by Ramon Parkins.


text:
Cardi B Pledges To Cover Funeral Costs For Bronx Fire Victims
Cardi B is helping cover the funeral and burial costs for the people killed in a recent building fire in her native New York City.
According to TMZ, the 'UP' rapper has partnered with the Mayor's Fund to Advance New York City, a not-for-profit organization – to make sure all of the families' needs are met.
"I cannot begin to imagine the pain and anguish that the families of the victims are experiencing, but I hope that not having to worry about the costs associated with burying their loved ones will help as they move forward and heal," she said. "I send my prayers and condolences to everyone affected by this horrific tragedy."
The January 9 fire, reportedly caused by a broken space heater claimed the lives of 17 people including eight children. Several others were left injured and are receiving treatment.
New York City mayor Eric Adams, per TMZ says "We are grateful for Cardi B, a real superstar on and off the mic, for granting some critical financial relief to families of the victims. The city will be forever thankful to her and also to the grassroots donors and corporate partners who have been able to offer immediate support for our neighbors in need, to reestablish themselves during this difficult time."
The "WAP" hitmaker is working on her sophomore album, the follow-up to her history making debut album, Invasion Of Privacy.
Tags: Cardi BNEWS
PARTYNEXTDOOR Feat. Drake & Bad Bunny "Loyal" Remix
NEW MUSIC: J. Cole Ft. Bobwire – The Reasons


I would like to... thank for my remaining alive till now. I'm grateful for the ability to speak and joy to listen and hear you.
The above confession was made as my reply to BlogDumps request to finish the sentence "I would like to...." As I listened to these fruits of my own lips, I saw the text in between the lines as a perfect title to the photo The Aged, which looks at you from the bottom of this post now. The picture enables as me as all possible its viewers to muse on... life and art / to dive into the world of hot emotions and wise symbols- to enjoy the metaphors that may come to mind while viewing.
Yet in reality this post was inspired not by my wish to portray all above to you , but to thank Top Sites Tuesday for the challenging theme (finish the sentence) that helped me to look at myself once again. I saw just my own emotions and didn't find any names of my beloved in the thanks I put on a web. That was awful, yet I am so grateful for the above experience, because the comprehension of my selfishness enabled me to address the doctor . Thus Blogdumps as if put me on a road of recovery....Thank you!
This is THAT, isn't it?
While looking at this photography, I surveyed my whole life. That was not a shot of a tree but the encounter with myself.
The tree of the picture was just a tree by a street, yet it became my personal diary now.
Such transformation is inescapable in any artwork. Just think. As one takes a picture of a house, it cease to be a house and becomes a symbol. While the house on a street provides the dwelling-space to us, the picture of the house demands such space itself. Each and all objects of any artwork should be accepted as the symbols. So if we enjoy the picture, we enjoy a story beyond the framed canvas. As I was caught by a view of a tree, I didn't examine the composition of a photography, but was thinking about myself - about my personal response to remaining alive till now.
A list of wood products looks as the numberless. Many enjoy that fine building material, yet how many of us recall that the trees weep if some snaps their branch?
The trees experience the same pain as we do when hurt our fingers. Likewise the flowers...the blossoms not only look fine but also talk- intercommunicate/inform each other about the people who are looking at them.
Thus a tree illustrates either our business or the morality, and the photography on above overburdens the viewers with the numberless questions.
The above may look like a mess, thus the wish to remove one's eyes would look natural, yet the picture from underfoot knocks on the heart. Do you see the portrait gallery there too? More I looked at the picture, more portraits were recognized. The dirt and dust disappeared - the picture transformed into a mirror.
if you enjoyed these my two thoughts, click on BlogDumps' Top Sites Tuesday button.I would greatly appreciate all your remarks and comments.
I studied a way from my bedroom to the kitchen table and learned it well. I can portray that highway of my hometown even with the closed eyes now. Have a look at the picture. I hope this road map will serve you well too.
At times "art by Tomas" may fail to inspire, but the reminder of the need to choose what to study for not end in a crash to the dusty road will always be beneficial. So I dared to showcase this pick.
The artworks are like the prompts to name what you see for the pictures could revive in you response as your own life harvest. Let your earnings be worthy your time. Let the sunset would not find you helpless.
I went outside and looked around. Thus this photography appeared - you are looking at the Price of Freedom now. The above description reveals a message of the picture that answers Top Sites Tuesday #55 theme - Your Fondest Memory of Your Home.
What's in between the lines?
That's Art by Tomas' look at oneself - the experience of sunset as a spring awakes the earth.
We can either talk about the photography and label its technical quality, or recognize what's beyond the image. We can either evaluate the flatness of the artistic surface, or recover from all our fears by viewing the purity and simplicity of art of being. We are free to choose. I prefer a sip of fresh air.
The artworks Wisdom Lover and Peace Addict (or any others from Art by Tomas blog) look differently between, but proclaim the same message: the creative work consists not from making the colorful surfaces, but the discovery of oneself in the objects we are looking at.
what is the meaning of the edge? Does it symbolize a curse, or guard me from my bodily wishes?
the living with the disabilities gifts time to me to uplift the eyes and thus helps to discover just stunning colors of the sunlight, the spiritual world.
The jobless needs to hurry nowhere, so I have lots of time to enjoy the miracle of the inner peace.
Yet the picture talks eloquently. Dog's eyes look sadly.
I fear to loose my eyesight too. That's why I am sharing my joy with you.
While the artwork should look attractively , the Art of Being enables the beholder to discover himself in other and thus transforms his self-pity into the discovery of the spiritual wonderland.
Thank you for reading my diary and your inspiring comments.
Each picture has a story and the title. That's life reality we encounter with and the interpretation of what we choose to see.
So while looking at the City Silence, I questioned myself was such possible at all? Can the city be silent?
The tree wounds catch the eye and look pictorially, yet such nature can't attract or inspire any. It is likewise with the photo on above. Though people with the disabilities (the sick and chronically ill - the retired) may easily identify themselves with the image and enjoy the photo as a symbol, the picture may be labeled as the kitsch / weak as the artwork- unworthy a penny ....like the beggars on a street. The stories of the dependent heroes are prominent only in the books with hard covers on our bookshelf.
So what's City Silence? Just a shot or what you hear inside?
As one with chronic illness, I was forced to look aimlessly at my window, yet the Internet freed me from that need. New technologies enabled me to forget the limitations of life with disabilities. I was revived by a sense of belonging to the world of bloggers. So I thank you the hot for the help to expand my horizons. Now even what was dark enough looks like the promise of the light. I am glad to rename the colors of my palette/ to see the diagnosis of human heart even in what looks like an accidental shot. So what is black?
Taking the photos demands from the photographer more conscious look than common gazing at what's before him and thus awakes/enables the photographer to discover his own inner light to shoot in totally inappropriate situations.That's the art of being.


Companies presently offering vacancies for Scientific roles in Brisbane CBD include Mater Health Services, Qsuper, Red Hat..
The standard deviation of salaries for the results of this search is 36,044.
There are 45 vacancies for Scientific roles in Brisbane CBD available immediately, compared to 46 in St Lucia.


If today is any indication of what tomorrow will be like, there is one thing for sure – the future will be mighty bright. Southern California's hottest, and youngest, up and com ing band, Thursdays in Suburbia is not your average kid band, in fact we would be remiss to suggest they are your average Rock Band. Thursdays in Suburbia sound more like a polished metal band than a few kids jamming in the neighbors garage. They have high-energy vocals, heavy bass lines, driving guitars riffs and percussion's that will blow most, if not all, adult drummers away.
How does a group of musicians play this well and sound so good? We are assuming with lots of practice and a support network of cool parents, music instructors and the self determination to be their very best.
Coming Up Magazine had the privilege to run a few questions by the band recently and we were blown away by their responses.
So how did you come up with the great name, Thursdays in Suburbia?
Our band practice is on Thursdays, and we live in the suburbs.
The five of us have been playing together since May.
We were introduced by our instructors at Rockstars of Tomorrow Music Academy.
Was there ever any question about what style of music you would play?
Yes! A lot! We all like to play different types of music. Some of us like country and rock. Liam and Leyla were in another group that pla yed alternative, so when Thursdays in Suburbia was formed that genre shifted over. We also vote in all of the songs that we choose.
Is it hard to get in enough practice and still be good students at school?
Yes, it can be hard sometimes. But most of us are on Honor Roll, so we work hard at both – school and music. We've had teachers come to our shows, and their encouragement means a lot to us. We all make sure that school is our first priority.
What do your friends think about you guys being in a band, I mean you have to be some of the only kids your age playing places like the House of Blues, right?
Our school friends think it's pretty cool, and sometimes they come to our shows. Our friends at Rockstars of Tomorrow perform a lot, too. All of the bands are supportive of one another, which is really cool. It's fun getting to go see our friends playing at different places, too.
What other things do you like doing in addition to playing music – are you into sports or anything?
Some of us like sports – soccer, football, and basketball. We all like camping and water sports like wakeboarding. Leyla rides a dirtbike. Coryn is in drama, too.
Would you say music is your favorite interest out of all of them?
Yes!! Some of us play multiple instruments, too. Coryn plays the trumpet, baritone, piano, bass and guitar. Leyla plays bass and guitar.
Who are some of your music heroes – better yet who are your heroes in general?
When you are not playing music, what kind of music are you listening to, Rock, Electronic, Country?
We listen to a wide variety of music. We like alternative, country, rock, classic rock, and blues.
Do you have any bands or musicians you are really into right now?
Muse, Guns n Roses, Red Hot Chili Peppers, Foo Fighters, My Chemical Romance, Flyleaf, AFI, Paramore, Evanescence, AC/DC, Santana and BB King.
Most of the songs are playing are cover songs, which are like really great. Have you thought about laying down a few of your own tunes?
Yes! We are working on writing original music. It's going really well and we're excited about what we have so far.
Can you give us an idea of what kind of music it is – is it edgy metal or more traditional Pop?
It's definitely not pop. The songs that we cover have influenced us a lot so we're using that sound to help us in our songwriting. It's "heavy alternative." We want to get people up and moving when we're playing.
Are you planning on recording a few of your songs soon, say maybe putting them on Bandcamp or iTunes?
Hopefully soon. We want to get into the recording studio at Rockstars and then maybe put our songs on Sound Cloud.
Where would you like to be next year with your band? Do you see yourselves Touring – hah!
By next year we would like to have more original music. We want to keep playing local venues, but touring to new places would be cool too. We want to be the best that we can be.
Well, that's almost it for now, but we do want to ask you, since our Magazine is Called Coming Up which is like moving forward in a positive way, we like giving shout outs to people. Are there any people or other bands or musicians you want to give a shout out to – say someone we should have a listen to?
There are a lot of awesome bands coming out of Rockstars of Tomorrow. Surge, Minor Strut, Roy G Biv, and Brianna Becerril and Radiodrone. They're out there performing all the time – check them out!
Thursdays in Suburbia is performing around town and hopefully near you soon. Check out their Facebook page and give 'em a holler.


Two plus years ago, Ecotrust and Ecotrust Canada sent Ian Gill, the founding President and 16 year veteran of Ecotrust Canada to start a third member of the Ecotrust family in Australia. A well-seasoned conservation economy visionary and champion, Ian set his sights on supporting indigenous communities in Northern Australia to document their territories and build economies of right type and scale for their place and their culture.
He and his team also laid a tremendous amount of groundwork for social finance in that country – recognizing the need for alternative forms of capital to support local and regional development. After a valiant effort, Ian has just announced to his staff, board and partners that the funding to sustain Ecotrust Australia's work is simply not to be found. A weakening philanthropic environment, continued uncertainty around our global economic health, and a mission that in some ways challenged the status quo of power and money in Australia has combined to bring an important organization, at a critical time in that Nation's history for aboriginal reconciliation and for thoughtful resource development, prematurely to its knees.
We are sorry to lose our 'legs' in Australia, and sad for the commitment and dedicated staff that have worked so hard to make a go of the mission of place-based development. We note that the closure of Ecotrust Australia in no way impacts the operations of Ecotrust or Ecotrust Canada – both of which maintain separate operating and governance structures appropriate to our countries of origin. At this time the longer term plans for Ian and family have not been announced but the Ecotrust Canada family will be very pleased if they elect to return home.


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