text:
New ASU scholarship helps eligible military students cover full tuition
ASU Online News Military
Arizona State University has announced a new way that the school is proud to support the troops: the Military Active Duty Reservist Commitment Scholarship.
The scholarship aims to help undergraduate military students cover the costs of ASU's online degree programs. It provides funding for out-of-pocket tuition expenses not already covered by military Tuition Assistance. This means some of ASU's top programs are now more accessible and affordable for those students who struggle to finance the rest of their education.
This new scholarship especially benefits those actively serving by not just diminishing the financial burden, but also providing the flexibility of remote access to course materials through our innovative online education platform. With ASU Online, military students can access online courses, no matter where they're stationed or residing.
It's part of ASU's ongoing mission to help put education back within reach for today's military students. Retired U.S. Army Lt. Gen. Benjamin C. Freakley serves as a special adviser to the university president for leadership initiatives and serves to help ASU elevate our standards as a pioneering institution. Freakley says it's our collective duty to fully support the education goals of those who serve our country, and ASU wholeheartedly agrees.
Find out how the Military Active Duty Reservist Commitment Scholarship has already impacted military students by reading the full article here.


where to place a single sub in a 5.1 system ?
any suggestions where to place a single sub in a 5.1 system ?
behind the speakers, in the corner ? any suggestions ?
hi mike. really it depends on your room and your prefernces, and the sub, to an extent. there is TONS of info on this subject and it all pretty much holds true. corners load the room and can be "boomy". out from the corner results in smoother response but less output. the method most often suggested, is placinf the sub in your listening position and crawl around the room till you hear the best sound and that is the idel place for the sub. go to av123.com and search subwoofer calibration. there is an extensive paper on the subject.
"There's no reason that corners lead to boomy bass or that out from corners results in smoother response."
sound that's most often described. In short, in-accurate distorted bass, that has many over-reinforced response peaks in the sound.
Now, unless your room is very small, and your corner placed subwoofer is then only causing a peaks and dips at only 2 or 3 frequencies, depending, you might find that there's only a bass hump of sorts, in regards to the response curve - in which case certain response attributes of your sub, your seating arrangements, and-or EQ settings (whatever) might not have you noticing too much unnatural sound and uneven response from the sub. However, placing the sub right at 3 room boundaries will, yes, most definitely cause you to have to somehow adress the bass response anomalies, for accurate response - even beyond simply adjusting output/volume levels.
One thing as an advantage (however, absolute phase is still to be considered, if not adressed) to placing subs in the corner, is that you can get more output and efficiency in the corner placement, and todays EQ's can do a superb job of smoothing out the response curve.
Anyway, I don't like the statement that "there's no reason that corner placement results in boomy bass". My experience says that does not compute.
Placement + integration/bass management = good bass.
Corners cutting will benefit no one. All rooms are different. Setting up sub - takes time and patients.
There are many ways to do it..........and few don'ts.


It's easy to choose a free tool when it comes to agile marketing project management. Yet if you use Trello in its original form, it's probably doing more harm than good when it comes to your transition into agile marketing.
A few months back, I enjoyed my experience at the HSMAI Digital Marketing Strategy Conference. The day kicked off with the keynote speaker singing the praises of agile marketing.
He then boasted that the software tool his team uses for an agile marketing workplace is Trello.
I groaned out loud. I couldn't—nay, didn't bother to—contain my annoyance.
Sure, Trello is free. But it's definitely not an agile marketing tool. And before you grab your metaphorical torches and pitchforks, let me argue my case.
I'm just going to give you the bare bones on agile marketing. There are a lot of bad explanations out there, so turn to TechTarget for a more thorough explanation.
Agile marketing is a workplace lifestyle change where the team focuses on transparency, responsiveness and cross-collaboration to get the job done.
Through the agile marketing method, the team breaks a project down together during a sprint planning meeting with the mindset that the entire team is responsible for completing the project on time. Each member takes on steps of the projects based on their availability and skills, then estimates the duration to complete each step during a set period of time called a sprint.
The team regularly meets at a daily scrum to discuss project status as well as brainstorm solutions to challenges stalling or could potentially stall progress. A sprint retrospective meeting is held once the sprint ends to review team progress as well as the impact of completed projects over time.
Agile marketing isn't an original idea. Its concept borrows from agile development methodologies. Many marketing teams found the need for agile marketing based on how rapidly marketing—especially digital marketing—changes.
Think I'm explaining it all wrong? I could be. I'm merely describing how I learned agile marketing process from my professional experiences.
Not many teams actually implement agile marketing correctly into their practices. But that's a blog post for another day.
If you're doing agile marketing, you need a tool that organizes projects, backlogs and tasks.
Guess what? You need an agile marketing tool.
Fortunately, many agile marketing tools let you test out the software for at least two weeks (if not for more than a month) to see if it fits the flow for your team's needs. It's a pretty standard practice for all forms of software products.
Yet many companies don't make full use out of this grace period. They immediately jump on the cheap option. What agile marketing tool can our team use for free?
The answer? None. At least what I can find.
Trello isn't an agile marketing tool. It's a project management tool being poorly manipulated for supposed agile marketing practices that quickly go south.
A project management tool is software that allows you to manage projects by tasks across teams.
Can you do that on Trello? Sort of.
You can make Trello do these tasks—if you know the workaround steps or consult the help page all the time. The Trello solution tends to be color-coding any and everything.
It's just not intuitive or originally intended for the agile marketing method.
A big mistake many companies make when transitioning to agile marketing is (unknowingly) doing it halfway. And that often starts by choosing the wrong software.
You must commit to an agile marketing tool that lets your team fully thrive within the agile marketing method. If the tool limits the ease of executing agile marketing practices, then corners get cut—until it brings your team back to where you started.
There's nothing wrong with practicing project management on Trello. In fact, more and more agile marketing tools easily integrate with Trello so that when you're ready to take that big step, you can easily integrate your team's current and past progress.
If your team is ready for the next step—and it pains me to say this—there is a way to convert Trello into a legit agile marketing tool.
There's a third-party extension called Scrum for Trello that adds in the intuitive features of agile marketing software (such as story points, progress bars and time spent) into the project management Trello experience.
Trello is only useful for agile marketing if and only if you install and integrate the Scrum for Trello extension on the first day of switching to agile marketing.
The truth still is that most agile marketing teams choosing Trello do not download this extension pack.
Moral of the story? Either transform Trello into a 100% agile marketing tool with extension packs or select an agile marketing tool designed for agile marketing practices.
I think you know which one I recommend.
Do you agree? Is Trello a project management tool or an agile marketing tool? Share your thoughts and strategies in the comments section below.
Are Split Infinitives Grammatically Correct?


text:
Amateur Adult Artistry: Drawing to Destress is a Draw
April 7, 2016 Mercedes Ortiz Features 0
Photo courtesy of Flikr user Starsapart.
Nothing suits 30-year-old Matthew Ryan Limmerick more after a long hard day of work than to sit, relax and color. His Harry Potter and "Game of Thrones" adult coloring books are therapeutic and allow him to take a breath and block everything else out.
Limmerick started using adult coloring books when they emerged out of the United Kingdom as anxiety treatment. His therapist advised coloring would relieve his anxiety and depression.
"My therapist recommended that I start coloring to destress and so I've been using them since then," Limmerick said.
Limmerick wears many hats. On top of being a full time college student taking on 20 credits, he works as a costume designer at SUNY New Paltz, a freelance designer, as well as a freelance book designer. He uses coloring books as a form of escape from his many demanding duties.
"I can set my work aside, go back to play with colors, play with schemes, texture, and things," Limmerick said. "And I use that in the work I am doing."
Dark colors like purples, blues, burgundy, gray and black are often found to fill Limmerick's books.
"A lot of times I end up going with these dark palettes," Limmerick said. "I don't know. It's where my mind sits."
Second-year creative writing major Mackenzie Lofrese said color schemes depend on how she feels when she sits down to color.
"Sometimes I'll do just cold colors," Lofrese said. "I'll do blues and greens, but I'm a big fan of orange. So sometimes I'll do whole sections of orange with different amounts of whites in it."
Second-year elementary education major, Christina Payne mentioned she uses greens and blues for her maritime coloring books and pinks and purples for the floral ones.
Barbara Moschitta, a certified dietician and nutritionist, believes the students' color choices depend on where they are mentally.
"I'm not well-versed in the 'goth crowd' where they tend to do dark colors and lighter, more effervescent personalities resonate in pastels," Moschitta said. "So I think it's a matter of taste and where they are in life."
Moschitta suggests people shouldn't depend solely on adult coloring books to relieve their stress and to use different forms of art.
"It doesn't have to be a coloring book," Moschitta said. "It can be just releasing yourself with color or a medium that feels right for you. It may not be paint. It might be working with fabric or it might be working with a different medium-like sculpture."
destress

redpajama_set_name: RedPajamaCommonCrawl


Custom Wedding Charging Stations Keep the Photos Coming!
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By the time your wedding reception starts, your guests phones will most likely be in need of a serious charge. Not only have they been taking numerous pictures of your beautiful ceremony but they probably uploaded them all to social media for you. Therefore, they are going to be looking to plug in. Rather than having your guests searching the walls for awkward places to plug in why not provide them with convenient wedding charging stations?
You'll have no worries with SSE's custom charging stations. First of all, they are rather small in size. Therefore, you won't have to worry about them taking up a bunch of space or distracting from your gorgeous decor. Also, these are quick charge stations with I-phone, Android and mini USB cables.
Yes, there are thousands of posts on Pinterest about creating your own. But do you really want to add another to thing to your to-do list? More than likely, this answer is no. So why not save yourself some time and stress and let Selective Sound take care of this for you. You want it customized just for you? No problem! We have you covered!


Are they going to continue to hide behind Sexual misconduct or will the truth come out?
In the end, all will be right.
Eric Holder & Rep Swalwell Panic? Game over.
[RR] req meeting FBI Deputy Director">#2 w/ POTUS DECLINED.


Maundy Thursday to the Easter Vigil is a continuos act of worship. It is best appreciated by being part of all three services.
The mood of Thursday evening's service starts positive and ends much more darkly and dramatically. We're with Jesus in the Upper Room. We remember his service of others through a simple act of foot washing, followed by a quiet celebration of the first Last Supper. Eventually, the lights are dimmed and the church is stripped bare of decoration. We're now in the Garden of Gethsemane and a Watch of Prayer continues until midnight.
Come and pray with Jesus.


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We are pleased to send you the new issue of Operators' ProNews, your dedicated newsletter that informs you on EVS' latest product releases and new product updates included in the different XT – XS Packages 12.05 released around Mid-2014.
This newsletter gives some information about the Multicam 12.05.35 that was released in May 2014. First on the technical side, where several new features have been implemented, such as the Integration of a new network board with 10 GbE connectivity on XT3 and XS servers that also offers the possibility of teaming the 1 or 10 GbE connections; the support of the new XS 4RU server, new supermotion configurations and hypermotion cameras, SD autosense and upscale to HD, the TwinRec mode to increase the number of ingest channels and a new dual HD Multiviewer.
On the operational side, the operator has now the possibility to clear record trains for maintenance operations and to define cue points on clips as well as a speedbreak functionality for Hypermotion cameras.
This section provides information on the latest release of the LSM Connect. Amongst the new features we have a new Graphical User Interface (GUI) that integrates Epsio FX with icons to represent video effects, the possibility to see and copy distant playlists (playlists stored on other servers on the network) and to preload a given playlist element.
For this new release of MultiReview the main changes are located on the Graphical User Interface (GUI) side where its ergonomics has been improved by integrating several new features. The Sync Replay mode has been renamed Smart Replay mode and offers three new features and improvements in Replay and Highlight modes.
This ProNews also presents the new Epsio FX solution dedicated to LSM operators to apply artistic replay effects and graphic transitions. It offers the possibility to enhance highlights within very short time and without any additional heavy infrastructure.
Epsio FX works in conjunction with the Multicam LSM and the LSM Connect applications and gives the operator a centralized interface to manage video effects.
Epsio Zoom has also intregrated a couple of new features. To read more about these and all the above described topics click on the link hereunder.
This newsletter includes information related to the IPDirector releases 6.41 and 6.55. Several changes and new functionnalities have been integrated into these releases, amongst the Autoclipping tool to transfer logs from an external logging or statistical system (e.g. Opta) into IPDirector, the possibility to use Xsquare targets for streams in the Ingest Scheduler, the Melt / backup workflow to archive a huge amount of clips as a single one and the Cascading Grid tool designed to guide the users, especially loggers and ingest operators, to easily and quickly assign keywords to media.
This is the case for the XFile3 with several improvements that offer the possibility to archive playlists, edit an existing rule in Auto Archive mode, name streams, add metadata to elements that are streamed or archived.
XFlyStreamer also offers new features amongst which the support of TWINREC recordings, AVID Metadata definition and better performances for XDCAM HD backups.


NO PURCHASE OR PAYMENT OF ANY KIND IS NECESSARY TO ENTER OR WIN. A PURCHASE WILL NOT INCREASE YOUR CHANCE OF WINNING. INTERNET ACCESS REQUIRED. SWEEPSTAKES SUBJECT TO OFFICIAL RULES.
SWEEPSTAKES PERIOD: The Sweepstakes begins February 12, 2018 at 9:00:00 a.m. Central Time ("CT") and ends March 2, 2018 at 11:59:59 p.m. CT (the "Sweepstakes Period"). The Sweepstakes Period is divided into nineteen (19) daily entry periods (each a "Day"). A "Day" is defined as the twenty-four (24) hours between 12:00:00 a.m. CT and 11:59:59 p.m. CT of the same day with the exception of the first day of the Sweepstakes Period which shall begin at 9:00:00 a.m. CT and end at 11:59:59 p.m. CT of the same day.
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text:
\section{Introduction}

\begin{figure}[t]
		\begin{center}
			\includegraphics[width=1.0\linewidth]{framework_compare.pdf}
		\end{center}
		\caption{
		(a) Previous framework without temporal feature refinement.
		(b) Our framework via  neural compression-based feature learning.
		(c) t-SNE \cite{tsne} visualization. $c^{gt}_t$ are sampled from clean video (\textit{hypersmooth}, Set8~\cite{fastdvd}).
		For $c_t$ and $\tilde{c}_{t}$, we add different additive white Gaussian noises (same noise $\sigma$ but different noise random seeds) to the same input video to sample these feature points. It shows that $\tilde{c}_{t}$ are more robust to noise and get closer to $c^{gt}_t$.
		(d) Performance comparison on video denoising (Set8, noise $\sigma = 50$). }
		\vspace{-3mm}
		\label{runtime}
	\end{figure}

 Video restoration aims to recover the high-quality video from the degraded input. Typical degradation includes various noises, rain, haze, etc. It has a wide range of applications,  but this problem is still under-explored.
Different from image restoration that focuses on the intrinsic proprieties in single image~\cite{mprnet}, video restoration relies more on extracting and utilizing temporal features for better quality.

Recent video restoration methods mainly focus on network structure design for better extracting temporal features. For example, RViDeNet~\cite{crvd} and EDVR~\cite{edvr} use deformable convolution to align the features of neighboring frames.
BasicVSR \cite{basicvsr} designs a bi-directional feature propagation network.
BasicVSR++ \cite{basicvsrpp} introduces the second-order grid propagation network structure and flow-guided deformable alignment network. However, these methods directly use the extracted temporal features without any refinement. The temporal features usually contain lots of noisy and irrelevant information, which interferes with the restoration of the current frame. In this paper, we take video denoising as a case study and explore how to   utilize the extracted temporal features efficiently.

We propose a novel neural compression-based solution to refine the features and learn  noise-robust feature representations.
From the perspective of neural codec, the noisy data usually contains lots of high-frequency and is hard to predict. To save the bitrate, codec prefers to discard these noisy and uncorrelated  contents.
This motivates us to design a neural compression module to purify the temporal features and filter the noisy information therein for video restoration.
To achieve robustness to noise, namely let the representations of the noise-perturbed data be mapped to the same quantized representation with the clean data with high probability, the quantization step needs to be properly set. However, most existing neural compression frameworks only support fixed quantization step size. This cannot meet our purpose and even  harms the inherent textures. To solve this problem,  we design an adaptive quantization mechanism at spatial-channel-wise for our compression module, where the quantization step is learned by our prior model.  Our quantization mechanism can adaptively purify the features with different content characteristics.
During the training, the cross-entropy loss is used to guide the learning of the compression module and helps preserve the most useful information.

Fig.~\ref{runtime} shows the framework comparison. From the t-SNE~\cite{tsne} visualization shown in Fig.~\ref{runtime} (c), we find, via our neural compression-based feature learning, the features are more robust to noise and get closer to the features generated from the clean video. Fig.~\ref{runtime} (d) is the performance comparison. We observe that, empowered by the noise-robust feature representations, our framework  significantly improves the restoration quality, when compared with prior state-of-the-art (SOTA) methods.
The major contributions of this paper are summarized as follows:

$\bullet$ We propose a novel neural compression-based feature learning for video restoration. After processed by our compression module, the features are more robust to noise and then improve the restoration quality.

$\bullet$ To achieve robustness to noise and adaptively purify the features with different content characteristics, we design a learnable quantization mechanism at spatial-channel-wise.

$\bullet$ To further boost the performance, we also design an attention module to  help the feature learning, and a motion vector refinement module to improve the discontinuous motion vector estimated from noisy video.

$\bullet$ We propose a lightweight framework. Compared with previous SOTA methods, our method achieves a better quality-complexity trade-off on video denoising, deraining, and dehazing.

\begin{figure*}
		 \begin{center}
		\includegraphics[width=\linewidth]{Motivation.pdf}
	   	\vspace{-0.9cm}

		\end{center}
		\caption{Comparison between traidtional codec x265 \cite{FFMPEG} and neural codec \cite{li2021deep} when compressing the noised video (additive white Gaussian noise with $\sigma = 20$). BPP denotes bits per pixel, which measures bitrate cost.
		}
		\label{Motivation_fig}
		\vspace{-4mm}
	\end{figure*}

\section{Related Work} \label{related}

\subsection{Video Restoration}
Existing video restoration methods leveraging temporal correlation can be divided into two categories: the sliding window-based methods and the recurrent methods.

The sliding window-based methods take several adjacent frames as input for each frame. Some methods~\cite{vnlnet, fastdvd} do not depend on explicit motion alignments. VNLNet~\cite{vnlnet} uses the non-local module to search similar patches across frames. FastDVDNet~\cite{fastdvd} uses stacked U-Net~\cite{unet} to progressively fuse the unaligned neighboring frames. By contrast,
ToFlow~\cite{tof} and DVDNet~\cite{dvdnet} use a motion estimation component to explicitly align neighboring frames.
To explore more temporal correlation, RViDeNet~\cite{crvd} and EDVR~\cite{edvr} propose
feature domain alignment. They align the features of neighboring frames rather than raw pixels
and this mechanism is adopted by most recent methods.

The sliding window-based methods suffer from a narrow temporal scope and cannot leverage the information outside the sliding window. By contrast, the recurrent methods learn the temporal features within a long temporal range, and achieve better performance. EMVD~\cite{emmvd} recurrently combines all past frames as auxiliary information.
Yan \textit{et al.} \cite{yan2019frame} proposed a recurrent feature propagation framework without explicit alignment.
The feature propagation in BasicVSR~\cite{basicvsr} uses the explicit alignment. Recently, BasicVSR++~\cite{
basicvsrpp} achieves excellent performance by using a second-order grid propagation structure and a flow-guided deformable alignment module.

\subsection{Video Compression}

Traditional video codecs, e.g, H.264 and H.265, adopt the hybrid framework which consists of prediction, transform, quantization, entropy coding, and loop-filter. Benefiting from the progress of neural image compression \cite{balle2017,balle2018variational,minnen2018joint}, neural video compression \cite{lu2019dvc,agustsson2020scale,lin2020m,liu2020conditional,li2021deep} recently also has a great development. For example, Lu \textit{ et al.} \cite{lu2019dvc} designed the DVC model, which follows the framework of traditional video codec but uses neural networks to implement all modules therein. Following DVC, Agustsson \textit{ et al.} \cite{agustsson2020scale} designed a more advanced optical flow estimation in scale space. Recently Li \textit{ et al.} \cite{li2021deep} proposed a conditional coding-based framework which achieves better performance.

\section{Motivation}\label{motivation_section}

Our motivation comes from that video compression can filter the noise. Video compression aims at using the least bitrate cost to represent the video.   For traditional codec, the residuals of noisy contents are usually large as they are hard to predict from  reference frames. These residuals contains lots of high-frequency and will consume many bits. To achieve the bitrate saving, traditional codec uses the quantization to discard the residuals of noisy contents, especially for the high-frequency therein, which is like a low-pass filter. We use the traditional codec x265 \cite{FFMPEG}  to conduct an analysis experiment, as shown in Fig.~\ref{Motivation_fig}.
From Fig. \ref{Motivation_fig} (c), we find that the traditional codec x265 can filter the noise in a large degree. Fig. \ref{Motivation_fig} (d) shows, when allocated more bits, x265 will encode the noise but in a much smoother way.

Different from traditional codec using linear DCT (discrete cosine transform), neural codec will learn a neural encoder to transform video from pixel domain to latent feature domain. The latent feature is then quantized, and its distribution is estimated to perform arithmetic coding.  The distribution is  predicted  more accurately, more bitrate saving is achieved. However, the distributions of  the noisy and uncorrelated  contents are hard to predict well. Thus, to save the bitrate,
these contents are more inclined to be discarded guided by the cross-entropy loss.  Fig. \ref{Motivation_fig} (e) shows the effectiveness of neural codec \cite{li2021deep} (model weights are provided by authors of \cite{li2021deep}). In particular, the neural codec can much better remove the noise therein and keep more semantic information when compared with
x265.

Inspired by this analysis, we propose utilizing a neural codec to help video restoration. The neural codec is used to filter the noisy information in features via the quantization.
If the quantization step and data distribution are properly learned,
 the representations of the noise-perturbed data will be mapped to the same quantized representation with the clean data with high probability.
The noise-robust feature representations will improve the final restoration quality.

Another advantage of using neural codec rather than traditional codec is that the neural codec can be end-to-end trainable and will have better performance when jointly trained with other restoration modules.

\begin{figure*}
		\begin{center}
			\includegraphics[width=\linewidth]{framework_1108.pdf}
		\end{center}
		\vspace{-0.4cm}
		\caption{The overall framework of our method. The detailed network structures of each module can be found in appendices.
		}
		\vspace{-3mm}
		\label{framework}

	\end{figure*}

\section{Proposed Method}\label{method}
\subsection{Framework Overview}
We design a neural compression-based framework for video restoration.
Our framework contains three parts: feature alignment, feature refinement for learning noise-robust feature representations, and feature fusion. The framework is illustrated in Fig.~\ref{framework}.

\textbf{Feature alignment.} Given the noisy frames $x_{t-1}$ and $x_t$, we first use motion estimation to estimate the motion vector (MV) $mv_{t}$. Then we design an MV refinement module to improve the discontinuous MV $mv_{t}$ estimated from noisy video. With the refined MV $\widetilde{mv}_{t}$, the coarse features $\hat{c}_{t}$ are obtained via a bilinear warping function

 \textbf{Feature refinement.}
As $\hat{c}_{t}$ contains some noisy and uncorrelated  information, we propose a neural compression-based feature refinement to purify the features. It is noted that, our feature refinement part consists of two modules. One is the attention module and the other is the neural compression module used for noise-robust feature learning.

 \textbf{Feature fusion.}
With the noise-robust features $\tilde{c}_{t}$ and the current frame $x_t$,
the final output frame $y_t$ is generated through the restoration module.
Besides $y_t$, the restoration module part will also generate the temporal features $c_t$ used for next step.

\begin{figure}[t]
	   \includegraphics[width=\linewidth]{flow.pdf}

		\caption{Example of MV comparison. We find MV is more accurate and more similar to the MV from clean frames after ME refinement. Zoom in for a better view.}
		\vspace{-3mm}
		\label{vis_flow}
\end{figure}

\subsection{Feature Alignment}\label{mvae}
To align the temporal features from last step to the current frame, we need to predict the MV. In our paper, we use the pre-trained optical flow estimation network SPyNet~\cite{spynet} as our motion estimation module.

However, estimating accurate MV from the degraded frames is quite difficult. As shown in Fig.~\ref{vis_flow} (a), the MV without any processing suffers from corruption and discontinuity, which is not accurate compared with the MV estimated from clean frames in Fig.~\ref{vis_flow} (c).
To solve this problem, we propose using an MV refinement module to improve the MV. The MV refinement module adopts a lightweight auto-encoder structure. It encodes the corrupted MV into compact representations and then decodes them to the refined MV. The detailed network structure can be found in appendices. As Fig.~\ref{vis_flow} (b) shows, with our MV refinement, the MV is cleaner and more similar to the MV from the clean frames.

\subsection{Feature Refinement via Neural Compression}\label{cr}

Previous recurrent methods directly fuse the current frame and the aligned temporal features without any refinement. Actually, the temporal features may still contain some noisy and uncorrelated  information, which disturbs the restoration of the current frame.

To solve this problem, we propose a feature refinement process to learn noise-robust feature representations. This process consists of two modules, i.e. attention module and neural compression module. For the attention mechanism, many papers \cite{hu2018squeeze,ZhangLLZF19,mei2020pyramid,niu2020single} have studied it and proved its effectiveness. Thus, we design an attention module to scale the temporal features to help the feature learning. To achieve a good trade-off between performance and complexity, we design an auto-encoder-based attention network whose detailed network structure can be found in appendices.

After the attention module, the temporal features $ \check{c}_{t}$ will be purified by the proposed neural compression module. Following the design in neural image/video compression \cite{balle2018variational,lu2019dvc,li2021deep}, our neural compression module consists of feature encoder-decoder, quantization process, and a prior model.

First, the temporal features $ \check{c}_{t}$ are encoded to be compact latent codes $e_t$ through the feature encoder:
\begin{equation}
	e_t = Encoder(\check{c}_{t}).
\end{equation}
To achieve the robustness to noise, the quantization is applied to $e_t$. The $e_t\in[s_k,s_{k+1})$ is quantized to value $\frac{s_k+s_{k+1}}{2}$, where $s_k$ and $s_{k+1}$ indicate the numerical range.
 Let $\ddot{c}_{t}=\check{c}_{t}+\epsilon$ be the noisy feature with noise $\epsilon$. Under the assumption that $Encoder$ is Lipschitz continuous, $\ddot{e}_{t}=Encoder(\ddot{c}_{t})$ and $e_t=Encoder(\check{c}_{t})$ will be located in the same region $[s_k,s_{k+1})$ with high probability if the quantization step   $s_{k+1}-s_k$ is relatively large, then they have the same quantized value. That means, the quantized representation is robust to noisy input. However, the robustness is determined by a prerequisite that the data distribution and quantization step   are properly learned.

 Most existing quantization solutions in neural image/video compression only use fixed quantization step. Actually, the content characteristics spatially vary in a large degree.
A fixed quantization step cannot handle various and complex contents well. For example, a fixed small quantization step fails to remove the noisy  information. A fixed large quantization step instead causes large information loss (i.e., the intrinsic quantization noise).
Thus, we propose an adaptive quantization mechanism, where the quantization step is learned. The illustration is shown in Fig. \ref{prior}.
First, $e_t$ are divided by the learned quantization step $q_t$ after subtracting the learned mean value $\mu_{t}$. The quotients are then rounded to the closest integers. At last, the quantized latent codes $\hat{e}_t$ are obtained via the opposite operations. The formulation is:
\begin{equation}\label{quant}
   \hat{e}_t = \lceil \frac{e_t-\mu_{t}}{q_t} \rfloor * q_t+\mu_{t}.
\end{equation}
 $\lceil \cdot \rfloor$ is the integer rounding operation.
 With the quantized latent codes $\hat{e}_t$, the noise-robust temporal features $\tilde{c}_{t}$ are then decoded through the feature decoder:
\begin{equation}
	\tilde{c}_{t} = Decoder( \hat{e}_t ).
\end{equation}

 	\begin{figure}[t]
 		\begin{center}
 			\includegraphics[width=\linewidth]{structure_adaptive_quantization.pdf}
 			\vspace{-8mm}
 		\end{center}
 	\caption{The illustration of adaptive quantization mechanism. CE means cross-entropy.
 		}
 	\vspace{-4mm}
 		\label{prior}

 	\end{figure}

As aforementioned, the data distribution and quantization step need to be properly learned to achieve noise robustness. In practice, we do not know the data distribution, thus we use the prior model to estimate it, and then use the cross-entropy loss to guide the learning of data distribution and quantization step. The  cross-entropy loss is  formulated as:
\begin{equation}\label{entropy_loss}
	\mathcal{L}oss_{CE} = E_{\hat{e}_t} [-log_2 p_{\hat{e}_t}(\hat{e}_t)],
\end{equation}
where $p_{\hat{e}_t}(\hat{e}_t) $ is the estimated probability mass function of the latent codes $\hat{e}_t$.
 In this paper, we follow ~\cite{li2021deep,pyt} and assume that $p_{\hat{e}_t}(\hat{e}_t)$ follows the Laplace distribution.
The prior model composed by neural network is used for estimating the distribution parameters. Detailed structures of the prior model can be found in appendices. But different from \cite{li2021deep,pyt} that only estimate the distribution parameters $(\mu_t,\sigma_t)$, our prior model also learns the quantization step size $q_t$. With $(\mu_t,\sigma_t,q_t)$,
the probability estimation of $p_{\hat{e}_t}(\hat{e}_t)$ is calculated as:
\begin{equation}\label{eqa_prior}
   	p_{\hat{e}_t}(\hat{e}_t) = \prod_i( \mathcal{L} (\mu_{t,i},\sigma^2_{t,i}) * \mathcal{U}(-\frac{q_{t,i}}{2},\frac{q_{t,i}}{2})) (\hat{e}_{t,i}),
\end{equation}
where $i$ specifies the spatial position of each element in $\hat{e}_{t}$.
According the probability mass function in Eq. \ref{eqa_prior}, we can calculate the cross-entropy loss via Eq. \ref{entropy_loss}.
The cross-entropy loss guides the compression module to learn proper data distribution and quantization step, and then achieves the robustness to noise.

In our framework, the quantization step $q_t$ is learnable at spatial-channel-wise. It can be adaptive to regions with different content characteristics. We visualize one channel example of quantization step map in Fig.~\ref{step_map}. The pixel intensity represents the size of quantization step. The larger pixel intensity represents the more noisy information that should be eliminated. As Fig.~\ref{step_map} shows, the quantization step size of the smooth region is usually larger because the noisy information therein is easier to remove. By contrast, the quantization step size of texture region (e.g., the table in the background poster, and there actually exist many details)  is usually smaller.
\begin{figure}[t]
   \centering
		\begin{center}
 			\includegraphics[width=\linewidth]{quantization.pdf}
 			\vspace{-10mm}
 		\end{center}
		\caption{Visualization example of the input frame and the corresponding learned quantization step size map. The frame is  from \textit{Johnny}, HEVC Class E dataset \cite{bossen2013common}.}
		\vspace{-4mm}
		\label{step_map}

	   \centering
\end{figure}
\subsection{Feature Fusion}\label{fusion}
The feature fusion part contains a restoration module. It will fuse the noise-robust temporal features $\tilde{c}_{t}$ with the current frame $x_t$, and then generates the final output frame $~\tilde{y_t}$. It is noted that, besides final output frame $~\tilde{y_t}$, the restoration module also generates the temporal features $c_t$ used for the next step,  like \cite{basicvsr}. Our restoration module is based on a lightweight U-Net~\cite{unet}. The detailed network structures can be found in appendices.

	\begin{table*}[t]
		\centering
		\scalebox{0.70}{
		\begin{tabular}{cccc ccc ccccc}
			\Xhline{1.0pt}
			 \textbf{$\sigma$} & \textbf{VNLnet~\cite{vnlnet}} & \textbf{DVDNet~\cite{dvdnet}} & \textbf{FastDVDNet~\cite{fastdvd}} & \textbf{EMVD-L~\cite{emmvd}} & \textbf{EMVD-S~\cite{emmvd}} & \textbf{EDVR~\cite{edvr}} & \textbf{BasicVSR~\cite{basicvsr}} & \textbf{BasicVSR++~\cite{basicvsrpp}} & \textbf{Ours}\\
			\Xhline{1.0pt}
			10    & 37.10/0.9637 & 36.08/0.9592 & 36.44/0.9624 & 36.56/0.9624 & 35.01/0.9442 & 37.16/0.9658 & 37.12/0.9674 & \textcolor{red}{\textbf{37.27}}/\textcolor{blue}{\textbf{/0.9682}}& \textcolor{blue}{\textbf{37.17}}/\textcolor{red}{\textbf{0.9684}} &\\
			20    & 33.88/0.9360 & 33.49/0.9307 & 33.43/0.9334 & 33.27/0.9320 & 31.65/0.8927 & 34.09/0.9379 & 34.13/0.9397 & \textcolor{red}{\textbf{34.25}}/\textcolor{blue}{\textbf{0.9411}} & \textcolor{blue}{\textbf{34.22}}\textcolor{red}{\textbf{/0.9437}}&\\
			30   & 31.95/0.9096      & 31.79/0.9023 & 31.68/0.9066 & 31.40/0.9032 & 29.94/0.8678 & 32.31/0.9125 & 32.33/0.9157 &
			\textcolor{blue}{\textbf{32.55}}/\textcolor{blue}{\textbf{0.9168}}&\textcolor{red}{\textbf{32.57/0.9184}}& \\
		   40   & 30.55/0.8814 & 30.55/0.8745 & 30.46/0.8812 & 30.05/0.8761 & 28.64/0.8328 & 31.02/0.8887 & 31.05/0.8929 &
		   \textcolor{blue}{\textbf{31.28}}/\textcolor{blue}{\textbf{0.8936}} &\textcolor{red}{\textbf{31.39/0.8970}}& \\
			50   & 29.47/0.8561   &   29.56/0.8480 & 29.53/0.8573 & 29.15/0.8528 & 27.83/0.8082 & 30.06/0.8660 & 30.11/0.8690   &\textcolor{blue}{\textbf{30.32}}/\textcolor{blue}{\textbf{0.8696}} & \textcolor{red}{\textbf{30.45/0.8770}}& \\\hline
			FLOPs (G) & - & - & 665 & 1106 & 5 & 3089 & 2947 & 3402 & 771 \\
			\hline
			\Xhline{1.0pt}
		\end{tabular}}
		\vspace{-2mm}
		\caption{PSNR/SSIM comparison with SOTA video denoising methods on synthetic dataset Set8. The best performance is highlighted in \textcolor{red}{\textbf{red}} (1st best) and \textcolor{blue}{\textbf{blue}} (2nd best). Our method achieves the best SSIM on all noise levels.} \label{davis}
		\vspace{-2mm}
	\end{table*}

		\begin{table*}[t]
		\centering
		\scalebox{0.74}{
			\begin{tabular}{ccc ccc ccc c}
			\Xhline{1.0pt}
			 & \textbf{FastDVDNet~\cite{fastdvd}} & \textbf{EDVR~\cite{edvr}} & \textbf{RViDeNet~\cite{crvd}} &   \textbf{EMVD-L~\cite{emmvd}} & \textbf{EMVD-S~\cite{emmvd}} & \textbf{BasicVSR~\cite{basicvsr}} & \textbf{BasicVSR++~\cite{basicvsrpp}} & \textbf{Ours} &\textbf{Ours-L} \\
			\Xhline{1.0pt}
			PSNR &44.30& 44.71 & 44.08 &      44.48 & 42.63 & 44.80 & \textcolor{blue}{\textbf{44.98}} & 44.72   & \textcolor{red}{\textbf{45.09}} \\
		   SSIM &0.9881 & 0.9902   & 0.9881   & 0.9895 & 0.9851 & 0.9903 & 0.9903 & \textcolor{blue}{\textbf{0.9906}} & \textcolor{red}{\textbf{0.9909}} \\
		   Runtime (ms) & 132 & 1511 & 1254 & 246 & 59 & 425 & 488 & 188 & 275 \\\hline

			\hline
			\Xhline{1.0pt}
		\end{tabular}}
        \vspace{-2mm}
		\caption{Comparison with SOTA video denoising methods on real-world dataset CRVD~\cite{crvd}.  Our method with default setting outperforms other fast methods  and gets close to the slow method. With a more powerful restoration module (i.e. 'Ours-L'), we can achieve SOTA performance in terms of both PSNR and SSIM.   The runtime is the average frame runtime for the whole dataset on a P100 GPU. } \label{crvd}
		\vspace{-5mm}
	\end{table*}

	\begin{table*}[t]
		\centering
		\scalebox{0.69}{
			\begin{tabular}{lcc ccc ccccc}
			\Xhline{1.0pt}
			 & & \textbf{MS-CSC~\cite{mscsc}} & \textbf{SE~\cite{se}} & \textbf{SpacCNN~\cite{spaccnn}} & \textbf{FastDerain~\cite{fastderain}} & \textbf{J4RNet-P~\cite{j4r} } & \textbf{FCRVD~\cite{fcrvd}} &\textbf{RMFD~\cite{rfmd}} & \textbf{BasicVSR++~\cite{basicvsrpp}} & \textbf{Ours} \\
			\Xhline{1.0pt}
			\multirow{2}*{RainSynAll100} & PSNR &    16.19 & 15.29 & 18.39 & 17.09 & 19.26& 21.06 & 25.14 & \textcolor{blue}{\textbf{27.67}} &\textcolor{red}{\textbf{28.11}} \\
		   & SSIM &   0.5078   & 0.5053 & 0.6469 & 0.5824 & 0.6238 & 0.7405 & \textcolor{blue}{\textbf{0.9172}} & 0.9135 & \textcolor{red}{\textbf{0.9235}} \\ \hline

		   \multirow{2}*{RainSynComplex25} & PSNR &    16.96 & 16.76 & 21.21 & 19.25 & 24.13& 27.72 & 32.70 & \textcolor{blue}{\textbf{33.42}} &\textcolor{red}{\textbf{34.27}} \\
		   & SSIM &   0.5049   & 0.5273 & 0.5854 & 0.5385 & 0.7163 & 0.8239 & 0.9357 & \textcolor{blue}{\textbf{0.9365}} & \textcolor{red}{\textbf{0.9434}} \\ \hline

			\hline
			\Xhline{1.0pt}
		\end{tabular}}
        \vspace{-2mm}
		\caption{Comparison with SOTA video deraining methods on RainSynComplex25~\cite{j4r} and RainSynAll100~\cite{rfmd}. We train the BasicVSR++~\cite{basicvsrpp} using the same setting as ours. Other baseline results are provided by RMFD~\cite{rfmd} paper.} \label{derain}
		\vspace{-2mm}
	\end{table*}

		\begin{table*}[t]
		\centering
		\scalebox{0.74}{
			\scalebox{0.97}{\begin{tabular}{ccc ccc ccc ccc}
			\Xhline{1.0pt}
			 & \textbf{DCP~\cite{dcp}} & \textbf{GDNet~\cite{gridn}} & \textbf{DuRN~\cite{durn}} &   \textbf{KDDN~\cite{kddn}} & \textbf{MSBDN~\cite{mbsdn}} & \textbf{FFA~\cite{ffa}} & \textbf{VDN~\cite{vdn} } & \textbf{EDVR~\cite{edvr}} &\textbf{CG-IDN~\cite{cgidn}} & \textbf{BasicVSR++~\cite{basicvsrpp}} & \textbf{Ours} \\
			\Xhline{1.0pt}
			PSNR &11.03& 19.69 & 18.51 &    16.32 & 22.01 & 16.65 & 16.64 & 21.22 & \textcolor{blue}{\textbf{23.21}} & 21.68 & \textcolor{red}{\textbf{23.63}} \\
		   SSIM &0.7285 & 0.8545 & 0.8272 &   0.7731 & 0.8759   & 0.8133 & 0.8133 & 0.8707 & \textcolor{blue}{\textbf{0.8836}} & 0.8726 & \textcolor{red}{\textbf{0.8925}} \\ \hline

			\hline
			\Xhline{1.0pt}
		\end{tabular}}}
        \vspace{-2mm}
		\caption{Comparison with SOTA video dehazing methods on REVIDE~\cite{cgidn} testset. We train the BasicVSR++~\cite{basicvsrpp} using the same setting as ours. Other baseline results are provided by CG-IDN~\cite{cgidn} paper.} \label{dehaze}
		\vspace{-5mm}
	\end{table*}

\subsection{Loss Function}
In our method, the loss function includes two items:
\begin{equation}
		\mathcal{L}oss= \sum_{t=1}^{n} \mathcal{L}oss_{\mathcal{L}2}(y_t, ~\tilde{y_t}) + \lambda \cdot \mathcal{L}oss_{CE}(\hat{e}_t).
\end{equation}
 $y_t$ and $~\tilde{y_t}$ are the clean and estimated frames, respectively.
$ \mathcal{L}oss_{\mathcal{L}2}$ is the L2 loss  and $\mathcal{L}oss_{CE}$ is the cross-entropy loss.
 To  learn noise-robust feature representation and then let it help the final  reconstruction,  we adopt a  two-stage training scheme and the details are in appendices.

\section{Experiment}~\label{experiment}
We evaluate our method on several video restoration tasks, including denoising, deraining, and dehazing.

\subsection{Dataset}
\textbf{Video denoising.} Both synthetic dataset and real-world dataset are tested. For the synthetic dataset, we follow the setting in FastDVDNet~\cite{fastdvd}. DAVIS2017 train-val set containing 90 videos is used for training. Set8 is used for testing. We add the additive white Gaussian noise (AWGN) to the clean video to synthesize the noisy video. Five noise levels, i.e. $\sigma$=10, 20, 30, 40, 50 are tested. For the real-world dataset, we follow the setting in EMVD~\cite{emmvd} and use the dataset from RViDeNet~\cite{crvd}.
It consists of a captured raw video dataset (CRVD) and a synthetic raw video dataset (SRVD).
Following EMVD and RviDeNet, we use CRVD scene 1$\sim$6 plus SRVD for training and CRVD scene 7$\sim$11 for testing.

\textbf{Video deraining.} Following ~\cite{rfmd}, we test our method on RainSynComplex25~\cite{j4r} and RainSynAll100~\cite{rfmd} datasets. RainSynComplex25 contains 190 videos for training and 25 videos for testing.
RainSynAll100 contains 900 videos for training and 100 videos for testing.

\textbf{Video dehazing.} We use REVIDE~\cite{cgidn} dataset which captures the pairs of hazy and corresponding haze-free videos in the
same scene by an acquisition system. It contains 42 videos for training and 6 videos for testing.

\subsection{Result on Video Denoising }
We compare our method with these baselines: VNLNet~\cite{vnlnet}, DVDNet~\cite{dvdnet}, FastDVDNet~\cite{fastdvd}, EMVD~\cite{emmvd}, EDVR~\cite{edvr}, BasicVSR~\cite{basicvsr}, BasicVSR++~\cite{basicvsrpp}, and RViDeNet~\cite{crvd}.
EMVD has several network structure configurations with different complexities. The large (EDVR-L) and small (EMVD-S) models are tested (more details about configurations are in appendices). The original BasicVSR/BasicVSR++ are bi-directional methods that leverage the temporal features from both the future and the past frames. To compare with other methods more fairly, we modified BasicVSR/BasicVSR++ to uni-directional methods that only use the temporal features from the past frames.

\textbf{Quantitative Comparison.}
We use peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) as quantitative evaluation metrics.
We present the results on synthetic noise video in Table~\ref{davis} and the results on real-world noisy video in Table~\ref{crvd}.
For the synthetic video, as Table~\ref{davis} shows, our method achieves the best SSIM on all noise levels. For PSNR, our method outperforms the second-best method BasicVSR++~\cite{basicvsrpp} with as least 0.11dB gain when the noise level is high ($\sigma$ = 40 or 50).
In addition, we   find   the quality improvement over BasicVSR++ is larger when the noise level is higher. It verifies that our proposed neural compression module can effectively filter the noise.
It is also worth noting that the FLOPs of our method is only 0.23 times of BasicVSR++, which shows that our method achieves a much better trade-off between quality and complexity.
Compared with the low-complexity methods FastDVDNet~\cite{fastdvd} and EMVD-L~\cite{emmvd}, our method achieves significant quality improvement.
For the real-world noisy video, it should be admitted that our method with default setting currently cannot outperform BasicVSR and BasicVSR++ in terms of PSNR but is better than them in terms of SSIM. When compared with low-complexity method FastDVDNet and EMVD-L, our method can achieve the best quality. Besides, if we change our U-Net-like~\cite{unet} restoration network to a W-Net-like~\cite{wnet} restoration network with more complexity (more details are in appendices), denoted as 'Our-L' in Table~\ref{crvd}, we can achieve the best PSNR and SSIM at the same time, but the complexity is still much less than that of BasicVSR and BasicVSR++.

\textbf{Qualitative Comparison.}
Fig.~\ref{fig4} shows the visual quality comparison.
As Fig.~\ref{fig4} shows,  FastDVDNet without feature alignment suffers from serious distortion in the text region.  The results of  BasicVSR++ are quite blurry caused by the propagated noise in temporal features.
By contrast, our neural compression-based method can learn noise-robust features and is able to restore much clearer textures.
More visual comparisons are in appendices.

\begin{figure*}[ht]
	\begin{center}
		\includegraphics[width=1.0\linewidth]{final_visual_compare.pdf}
	\end{center}
	\vspace{-6mm}
	\caption{Denoising: \textit{motorbike} video from  Set8 testset with noise variance 50. Deraining:   \textit{0985} video  from RainSynAll100 testset. Dehazing: \textit{L006} video from REVIDE dataset.
}
	\label{fig4}
	\vspace{-3mm}
\end{figure*}

		\begin{table}[t]
		\centering

		\scalebox{0.92}{\begin{tabular}{l|cccccc}
			\Xhline{1.0pt}
		    & $M_a$ & $M_b$ & $M_c$ & $M_d$ \\
			\hline\hline

		    \textbf{MVR} &    & \checkmark            & \checkmark & \checkmark   \\
		    \textbf{NCFL} &    &             & \checkmark & \checkmark\\
		    \textbf{FA} &    &                               &   & \checkmark \\\hline
      PSNR   & 29.75 & 29.87 &         30.29   & 30.45 \\

			\Xhline{1.0pt}
		\end{tabular}}
		\caption{The ablation study on different modules. Tested on Set8 ($\sigma = 50$).
		MVR is the MV refinement, NCFL is the neural  compression-based feature learning. FA is the feature attention. } \label{ablation}
	\end{table}

		\begin{table}[t]
		\centering
		\scalebox{0.92}{
		\begin{tabular}{l|ccc}
			\Xhline{1.0pt}
		    & \textbf{NCFL-AdapQ} & \textbf{NCFL-FixedQ} & \textbf{NCFL-NoQ} \\
			\hline\hline
      PSNR    & 30.29 & 29.86   & 29.98 \\

			\Xhline{1.0pt}
		\end{tabular}}
		\caption{The ablation study on quantization. Tested on Set8 ($\sigma = 50$).
	      NCFL-AdapQ is our default model with adaptive quantization, i.e. $M_c$ in Table \ref{ablation}. NCFL-FixedQ means we use the fixed quantization step as in existing neural video codecs. NCFL-NoQ removes the quantization and  is only a vanilla auto-encoder. } \label{cae}
	      \vspace{-2mm}
	\end{table}

\subsection{Result on Video Deraining }
We compare our method with prior SOTA video deraining methods, including MS-CSC~\cite{mscsc}, SE~\cite{se},  SpacCNN~\cite{spaccnn}, FastDerain~\cite{fastderain}, J4RNet-P~\cite{j4r}, FCRVD~\cite{fcrvd}, RMFD~\cite{rfmd}, and BasicVSR++~\cite{basicvsrpp}.
Since RainSynAll100 uses the rain accumulation degradation to generate the rainy video,  parts of the baseline methods including SE, MS-CSC, SpacCNN, and FastDerain could not handle this degradation thus MRF~\cite{mrf} is used as post-processing. More details could be found in~\cite{rfmd}.
FCRVD, RMFD, BasicVSR++, and our method could handle this degradation without extra post-processing. As Table~\ref{derain} shows, BasicVSR++ beats RMFD in terms of PSNR and SSIM on RainSynComplex25 but is slightly worse than RMFD in terms of SSIM on RainSynAll100. By contrast,  with the robust temporal features,  our method achieves the best PSNR and SSIM on both datasets.  Our method brings PSNR gain of 0.44 dB and SSIM gain of 0.0063 on RainSynAll100. We also
test RainSynLight25~\cite{j4r} and NTURain~\cite{spaccnn}. Their  results are provided in appendices. Fig.~\ref{fig4} also shows the visual quality comparison. We can see that our model could   remove   rain streak well, and produces   clearer and  more visual pleasing results.

\subsection{Result on Real-World Video Dehazing }
Table~\ref{dehaze} shows the comparison between our method and prior SOTA real-world video dehazing method: DCP~\cite{dcp}, GDNet~\cite{gridn}, DuRN~\cite{durn}, KDDN~\cite{kddn}, MSBDN~\cite{mbsdn}, FFA~\cite{ffa}, VDN~\cite{vdn}, EDVR~\cite{edvr}, CG-IDN~\cite{cgidn}, and BasicVSR++~\cite{basicvsrpp}. As Table~\ref{dehaze} shows, BasicVSR++ outperforms EDVR but is worse than MBSDN and CG-IDN that are specially designed for dehazing task. By contrast, our method has 0.42 dB PSNR and 0.0089 SSIM
improvements when compared with the second-best method CG-IDN.
In addition, the parameters of our method are 16M, and only 0.70 times of CG-IDN that has 23M parameters.
As Fig.~\ref{fig4} shows, the result of our method is more visual pleasing.

\subsection{Ablation Study}
This paper proposes three key modules: the MV refinement (MVR) for improving MV, the neural compression-based feature learning (NCFL) incorporated with adaptive quantization, and feature attention (FA). We study the effect of these modules and report the results in Table~\ref{ablation}. Without MVR, NCFL, and FA, the baseline model only contains a motion estimation module, bilinear warping, and a restoration module.

\textbf{MV refinement (MVR).} As Table~\ref{ablation} shows, the baseline model $M_a$ only achieves PSNR 29.75dB. It suffers from the discontinuous MV estimated from noisy video. When enabling our MVR, the MV is refined and $M_b$ reaches PSNR 29.87 dB. Our MVR  brings 0.12 dB PSNR improvement.

\textbf{Neural compression-based feature learning (NCFL).} If we further combine NCFL and MVR, $M_c$ reaches PSNR 30.29 dB and improves 0.42 dB compared with $M_b$. The significant improvement  verifies that the effectiveness of  NCFL. In addition, we also study two variants of NCFL. As Table~\ref{cae} shows, the PSNR of a vanilla auto-encoder without quantization (i.e. NCFL-NoQ) drops to 29.98dB.
This denotes that the improvement brought by NCFL mainly comes from the adaptive quantization mechanism rather than the increase of model parameters. In addition, we also test NCFL-FixedQ where a fixed quantization step is used as many existing neural video codecs do. The PSNR of NCFL-FixedQ drops to 29.89 dB. Its performance is even worse than NCFL-NoQ. This shows that a fixed quantization step instead loses some useful information and fails at learning noise-robust representations. By contrast,
a learnable quantization step at spatial-channel-wise can adaptively filter the noise and purify the temporal features with different content characteristics, which is quite important.

\textbf{Feature attention (FA).} In this paper, we also propose a FA module to further help the feature learning. As Table \ref{ablation} shows, $M_d$ achieves PSNR 30.45 dB. FA boosts the PNSR by 0.16 dB, which shows its effectiveness.

\subsection{NCFL on Different Degradations}
Table \ref{ablation} and Table \ref{cae} invesitage NCFL under AWGN degradation.
However, our NCFL is not confined to AWGN. It is also very effective for other complex degradations, such as real-word denoising, deraining, and dehazing.
Table \ref{multiTypes}  shows the comprehensive study on multiple degradations. For example, the comparison between \textit{$M_1$} and \textit{$M_3$}  shows that NCFL can achieve 0.81 dB gain for deraining. These substantial improvements verify the effectiveness of our NCFL. In addition, the comparison between \textit{$M_2$} and \textit{$M_3$} shows that cross-entropy loss can effectively guides the learning of NCFL under multiple degradations.

		\begin{table}[t]
		\centering
		\scalebox{0.85}{
			\begin{tabular}{lcccc}
            \Xhline{1.0pt}
             & Deraining &  Dehazing & RWD & AWGN \\\hline
			  \textit{$M_1$}: w/o NCFL  & 27.30 & 23.07 & 44.48 & 29.99   \\
			  \textit{$M_2$}: w/ NCFL (w/o CE) & 27.64 & 23.30 & 44.56 & 30.20 \\
			\textit{$M_3$}: w/ NCFL & 28.11 & 23.63 & 44.72 & 30.45 \\

			\Xhline{1.0pt}
		\end{tabular}}
		\caption{Study on NCFL under different degradation types. CE means the cross-entropy loss. RWD means real-world denoising. AWGN indicates additive white Gaussian noise.  }
		\label{multiTypes}
	\end{table}

\subsection{Bi-directional Video Denoising}
In previous experiments, we focus on the uni-directional setting where the temporal features only come from the past time. For the bi-directional setting, both the temporal features come from the past time and the future time can be used. One advantage of our method is that our method can be extended to bi-directional  setting easily.
We test the bi-directional model of BasicVSR~\cite{basicvsr}, BasicVSR++~\cite{basicvsrpp}, and our bi-directional model. The PSNR and complexity comparison is shown in Table~\ref{bidirection}. As Table~\ref{bidirection} shows, the bi-directional setting brings BasicVSR 0.59 dB gain, BasicVSR++ 0.78 dB gain, and our method 0.76 dB gain  with about 2x complexity. Under bi-directional setting, our  method still outperforms BasicVSR++ by 0.11 dB with only 0.21x FLOPs.
		\begin{table}[t]
		\centering
        \scalebox{0.90}{
		\begin{tabular}{cccc}
			\Xhline{1.0pt}
		    \textbf{Direction}& \textbf{Method} & \textbf{PSNR} & \textbf{FLOPs (G)}\\
			\hline\hline

		Uni-direction & BasicVSR   & 30.11 &   2947          \\
		   Uni-direction & BasicVSR++ & 30.32 &   3402          \\
		   Uni-direction & Ours &   30.45   & 771   \\\hline
		    Bi-direction& BasicVSR &    30.68 &   5855      \\
		    Bi-direction& BasicVSR++ &   31.10 &   7097      \\
          Bi-direction& Ours   & 31.21 &   1522 \\

			\Xhline{1.0pt}
		\end{tabular}}
		\caption{Bidirectional video denoising on Set8 with $\sigma$ = 50. } \label{bidirection}
	\end{table}

\section{Conclusion and Limitation}~\label{conclusion}
In this paper, we have designed a neural compression-based video restoration framework. We are inspired by the fact that neural video codec can naturally filter the noise, and then propose using neural compression to purify the temporal features and learn noise-robust feature representations. To solve the problem that the fixed quantization step harms the inherent textures, we propose a  learnable quantization mechanism at spatial-channel-wise to achieve robustness to noise.
At the same time, an attention module and an MV refinement module are proposed to further boost the performance.
 Experimental results show that the proposed method achieves a much better quality-complexity trade-off than previous SOTA methods.

Although our method is faster than most previous SOTA methods,  the inference speed of our method still does not meet the requirements of real-time scenarios. In the future, we will continue to improve the efficiency of our method for real-time video restoration.

{\small
	\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


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The capital of New Jersey, Trenton (population 85,000) has long been known as a port city and a transportation hub, as well as a link to Early American history. A stomping ground for George Washington's Continental Army, Trenton captures a piece of the past in its location along the Delaware River. The downtown area is commercial with restaurants, shops and a number of businesses.
The New Jersey State Museum, featuring Native American artifacts and Revolutionary War relics as well as the Trenton City Museum are good choices for visitors wanting to learn more about the history of the area.
Trenton also has a number of live music and theater options from Sovereign Bank Arena to the Mill Hill Playhouse. For history buffs, the Trenton War Memorial in the state capital complex honors the locals who died in WWI, and the Trenton Battle Monument serves to commemorate the battles that took place on the grounds during the Revolutionary War.
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NEW YORK (Reuters Life!) - Spanish tapas and Vietnamese pho are a world apart in origin and taste. Two chefs who have helped to popularize these dishes in the United States were nominated on Monday as tops in their field.
The James Beard Foundation said Spanish native Jose Andres who owns Minibar in Washington and Charles Phan who was born in Vietnam and operates The Slanted Door in San Francisco were nominated for its outstanding U.S. chef award.
"Our mission is to celebrate our culinary diversity, which reflects our diversity as a nation," said Susan Ungaro, the foundation's president.
The other three finalists were born in the United States and known for their contemporary American foods: Tom Colicchio of Craft in New York; Suzanne Goin of Lucques in Los Angeles and Gary Danko with his namesake eatery in San Francisco.
The James Beard awards, established in 1990, are the most prestigious cooking honors in the United States. The nominations were announced in New Orleans, while the awards will be given in New York on May 2 and May 3.
This year's top restaurant nominees have familiar vibes: Italian (Babbo in New York and Spiaggia in Chicago); French (Daniel in New York) and American (Boulevard in San Francisco and Highlands Bar & Grill in Birmingham, Alabama).
The top new U.S. eateries cited are mostly either in New York — Marea and Locanda Verde, or San Francisco — Flour + Water, Frances and RN74. Bibou in Philadelphia was also nominated as top new restaurant.
James Beard nominations have traditionally been dominated by chefs who specialize in American, French and Italian fares.
Phan's and Andres' top chef nominations reflect the ever-growing popularity of Southeast Asian cooking and enthusiasm for cutting-edge dishes in the United States.
Phan's nod is "symbolic of how well known and popular his Slanted Door cuisine has become," Ungaro said, referring to Phan's restaurant.
As for Andres, "his restaurants feature an exciting menu of molecular gastronomy with Spanish flair," she added.
Phan and Andres have been long-time U.S. residents, each operating multiple, successful restaurants.
Andres, now 40, moved to the United States in 1990, while Phan and his family immigrated to San Francisco in 1977.


This is what I wore for basically a casual weekend day, visiting family etc and going for lunch with my boyfriend etc, perhaps it is a bit dressy but I am definitely the type to over rather than under dress at every opportunity.
I am wearing the same clothes over and over again, I know, but I am so obsessed with this blazer, and I think it looks really nice over a plain jeans type outfit as well as patterned stuff. I am also loving the mules with jeans combination.
Can't believe how much of this outfit is Primark, this is very rare for me, but lately it has just been so good. I am having major regrets over not buying the shoes I mentioned in my previous post, they were about £6 and I've seen them in other shops for way, way more. What was I thinking? I am pretty sure they will only be available in size 8 when I go back because this always seem to be the case with Primark. Someone needs to re-asses how many of us girls actually have size 8/9 feet, you're definitely overestimating Primy! More fives plz.
You look really lovely, I love a jeans and mules combo too and I need to get myself one of those blazers!
I love getting dressed up so I'm exactly the same haha. I'd much rather be too dressy than far too casual if given the option.


This was a rainbow kind of day with warm sunlight followed by quick bursts of frantic rain. Look at the bright grey of the sky behind Addyman Books though the road was dazzling in the early evening sunshine. This is a post of our main shop as a backdrop to a very important book.
As a teenager I read, re-read and adored 84, Charing Cross Road by Helene Hanff. I dreamt of working in the Charing Cross Road and handling old books and sending them off across the world.
When I graduated I got a job there – 48a rather than 84 (now one of those pricey cake and coffee shops). Within a week I was helping a film prop lady find books for the movie of the book. The Complete Angler in the film was ours and a few others. I really thought I had the best job in the world. The hours were long and the pay was minuscule but I couldn't wait to get to work.
A few months later I was moved to Hay for a six week training programme. I never left and within a few months had started a small shop with Derek Addyman. Slowly we grew from a 10 x 10 room to the three shops which we love wholeheartedly.
Without Hanff's book my life could have been very different. I've never regretted my choices. If you can make your hobby and passion into your work your life makes sense.
This is a great article based on scientific principles. Thanks Dan for going through the eflA.trofong the same lines, this is why SWAT teams have gone away from and the military is going away from dynamic clearing of structures.


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Charities We Love
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Around the world millions of girls lack access to education. Our production All Our Yesterdays has donated profits from our Toronto Fringe run and Next Stage Theatre Festival run to Plan Canada's Because I Am A Girl and Save the Children, respectively. Below are four of the many organizations dedicated to bringing girls access to education, among other initiatives.
Save the Children Canada is a member of Save the Children International, a network of 30 member organizations raising funds and operating programs domestically and internationally that focus on the issues of health and nutrition, education, HIV and AIDS, child protection, livelihoods and food security, emergency relief and child rights governance. The members of Save the Children International work together as a federation by pooling resources, establishing common positions on issues and carrying out joint projects. Save the Children is the world's leading independent organization for children and is committed to ensuring children realize the rights to which they are entitled under the UN Convention on the Rights of the Child.
Because I am a Girl is Plan's global initiative to break the cycle of poverty and gender discrimination. Plan is a global movement for change, mobilizing millions of people around the world to support social justice for children in developing countries.
Girls and women are among the most disadvantaged around the world, denied access to education and exposed to unique barriers that keep them from attaining their full potential.
Because I am a Girl focuses on issues that directly affect girls around the world: education, child marriage, and gender-based violence.
Inspired by co-founders Malala and Ziauddin Yousafzai, Malala Fund's goal is to enable girls to complete 12 years of safe, quality education so that they can achieve their potential and be positive change-makers in their families and communities. We work with partners all over the world helping to empower girls and amplify their voices; we invest in local education leaders and programmes; and we advocate for more resources for education and safe schools for every child.
Almost 300 young women were kidnapped by Boko Haram from Chibok, Borno state in northeastern Nigeria in April 2014. 58 of those young women escaped their captors. 23 of those who escaped are now back in school at the American University of Nigeria.
Thanks to hundreds of gifts from generous donors all over the world, including two extraordinary anonymous philanthropists, the American University of Nigeria Foundation have been able to raise enough funds to provide scholarships for the entire academic careers of the 23 escaped Chibok students at AUN.


On Tuesday the euro/dollar closed down. The weakening of the euro against the dollar was caused by a correctional phase for the euro/pound. The euro pound fell 98 points to 0.8665. The price corrected by 38.2% upwards from 0.8517 to 0.8763. The euro rate fell from a maximum of 1.0627 to 1.0556 (-71 points) against the dollar. In Asia the pair's fall hastened to 1.0536.
On Wednesday trader attention is on the press conference for the US' new president. I didn't want to make a forecast, but a really interesting picture has been forming on the hourly graph. The price could bounce from 1.0536 or 1.0519. I've gone for 1.0519. I reckon that before Trump speaks we will see a fall for the euro. Then we may see a test of 1.0610 (at 67 degrees and the trend line) which was broken yesterday.
Intraday forecast: minimum: n/a, maximum n/a, close: n/a.
Yesterday's euro expectations came off. The price fell to the LB due to the euro/pound correction. So what can we expect from the euro today? The day will be unpredictable due to Trump's press conference. Nevertheless, I've made a forecast for before the press conference.
The trend line was broken at 1.0564. In accordance with the break, the closest target is 1.0482. The minimum from 5th January is at this level (see graph). On the way down the sellers need to pass a few resistances: 1.0535, 1.0518 and 1.0498. These are the targets for the sellers today.
Using the cycles I've arrived at a bounce with a strengthening of the euro to 1.0610. The price could bounce from the current level or from 1.0519. I've chosen 1.0519. At the very least, the 90th degree passes through it. The support is stronger than 1.0535. If the price lags in its growth from the forecast, it'd be better to give trading a miss today.
Source: "EUR/USD: euro ready to return to 1.0610"


iPhone 7 Repair Services - Screen Replacement, Charging Port, etc.
The Apple iPhone 7 screen replacement glass, touch screen, and 3D touch sensor are fused together in the factory and are provided as one piece. The replacement costs for a new screen replacement or other iPhone 7 repairs are listed below. This includes the replacement part, labor, and a 90 day warranty.
Will a Screen Replacement Fix Your iPhone 7?
Glass Touch Screen: If the touch screen (glass top) is cracked or broken with shattered glass, or your iPhone 7 has a non responsive touch screen, but the image under it is fine.
LCD Only: If you do not see anything when your iPhone 7 is on, but you hear sounds and /or vibration and other accessories work (Bluetooth, airplay, etc.).
The iPhone 7 has Touch ID security within the home button. The home button ribbon cable is exclusive to each device, and if replaced, will not provide Touch ID functionality. Replacement of the home button on the iPhone 7 will remove Touch ID functionality but will provide home button functionality.
iPlayPower will not be held responsible for iPhone 7 devices with torn or damaged Home Button security ribbons.
Repair service for your iPhone 7 plus back camera.
Repair service for your iPhone 7 back camera.