With the fast development of imaging and video technologies, images and video play a key role as a mechanism of information exchange, transmission or storage nowadays. Image or video compression systems, as well as compression quality evaluation systems have been widely deployed in many aspects. Due to the wide variety of content types of the input images, it is quite challenging for the compression quality evaluation system to reliably evaluate the compression quality. Sometimes it can be beneficial to use certain features of an input image or frame to configure or control such system.
Various techniques have been developed in this aspect and references considered to be relevant as background to the presently disclosed subject matter are listed below. Acknowledgement of the references herein is not to be inferred as meaning that these are in any way relevant to the patentability of the presently disclosed subject matter.
“Picture-graphics color image classification”, S. Prabhakar, H. Cheng, J. C. Handley, Z. Fan, and Y. Lin, in Proc. ICIP (2), 2002, pp. 785-788 discloses that high-level (semantic) image classification can be achieved by analysis of low-level image attributes geared for the particular classes. There is proposed a novel application of the known image processing and classification techniques to achieve a high-level classification of color images. The image classification algorithm uses three low-level image features: texture, color, and edge characteristics to classify a color image into two classes: business graphics or natural picture.
“Novel method for classification of Artificial and Natural images”, Nikkoo Khalsa. Dr. Vijay T. Ingole, International Journal of Scientific & Engineering Research, Volume 5, Issue 3, March-2014 discloses that the classification of images based on semantic description is a demanding and imperative problem in image retrieval system. Solitary easy features are extracted from the raw images data in order to exploit the dissimilarity of color pattern and spatial co-relation of pixels in Artificial and Natural images. These features have poor precision if used alone but when considered collectively, it forms a more complex and accurate global classifier with enhanced precision. There is described a method for classification of Artificial and Natural images with greater precision and low error rate.