By Wen Gao, Siwei Ma
This booklet provides an summary of the state-of-the-art in video coding know-how. in particular, it introduces the instruments of the AVS2 common, describing how AVS2 might help to accomplish an important development in coding potency for destiny video networks and functions via incorporating smarter coding instruments comparable to scene video coding. positive factors: introduces the fundamental options in video coding, and offers a brief historical past of video coding expertise and criteria; studies the coding framework, major coding instruments, and syntax constitution of AVS2; describes the foremost applied sciences utilized in the AVS2 commonplace, together with prediction coding, rework coding, entropy coding, and loop-filters; examines effective instruments for scene video coding and surveillance video, and the main points of a promising clever video coding process; discusses optimization applied sciences in video coding platforms; presents a overview of picture, video, and 3D content material caliber evaluation algorithms; surveys the recent study themes in video compression.
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Extra resources for Advanced Video Coding Systems
1 Prediction Coding The basic idea of prediction coding is to transmit a differential signal between the original signal and a prediction for the original signal, instead of the original signal. Fig. 9 Development of coding tools 28 2 Video Coding Basic Principle The differential signal is also called residual signal and at the receiver side the original signal can be reconstructed by adding the residual and the prediction. Compared to the original signal, the residual signal has lower correlation.
500-13 (2012), double-stimulus impairment scale (DSIS) method and the double-stimulus continuous quality-scale (DSCQS) method as well as alternative assessment methods such as single-stimulus (SS) methods, stimulus-comparison methods, single stimulus continuous quality evaluation (SSCQE) and simultaneous double stimulus for continuous evaluation (SDSCE) method are standardized for the quality assessment of television pictures. However, the subjective test usually costs many human and material resources, thus it cannot be used in real-time applications.
The SAO reduces the compression artifacts by first classifying reconstructed samples into different categories, obtaining an offset for each category, and then adding the offset to each sample. Compared to SAO with only one offset for each sample, the ALF processes one sample with neighboring samples by a multiple taps filter, parameters of which are obtained by minimizing the distortion between the distorted reconstruction frame and the original frame. , Quadtree-based ALF (Chen et al. 2011) and LCU-based ALF (Tsai 2012).