By Wen Gao, Siwei Ma
This publication offers an summary of the cutting-edge in video coding know-how. particularly, it introduces the instruments of the AVS2 average, describing how AVS2 can assist to accomplish an important development in coding potency for destiny video networks and purposes by means of incorporating smarter coding instruments comparable to scene video coding. beneficial properties: introduces the fundamental suggestions in video coding, and offers a brief background 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 usual, 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 approach; discusses optimization applied sciences in video coding platforms; offers a evaluation of photo, video, and 3D content material caliber evaluate algorithms; surveys the recent learn issues in video compression.
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Additional resources for Advanced Video Coding Systems
264/AVC. IEEE Trans Circuits Syst Video Technol 13(7):604–613 Ye Y, Karczewicz M (2008) Improved h. 264 intra coding based on bi-directional intra prediction, directional transform, and adaptive coefficient scanning. In: 15th IEEE international conference on image processing, ICIP 2008. IEEE, pp 2116–2119 Yue H, Sun X, Wu F, Yang J (2012) Sift-based image compression. In: 2012 IEEE international conference on multimedia and expo (ICME). IEEE, pp 473–478 Zhao X, Zhang L, Ma S, Gao W (2012) Video coding with rate-distortion optimized transform.
1 Introduction 37 and organize the encoding data (Yu et al. 2012). First, pictures are split into largest coding units (LCUs), which consist of 2N × 2N samples of luminance component and associated chrominance samples with N = 8, 16 or 32. One LCU can be a single CU or can be split into four smaller CUs with a quad-tree partition structure; a CU can be recursively split until it reaches the smallest CU size limit, as shown in Fig. 2a. Once the splitting of the CU hierarchical tree is finished, the leaf node CUs can be further split into PUs.
Temporal multi-hypothesis mode combines two predictors along the pre-defined temporal direction, while spatial multi-hypothesis mode combines two predictors along the pre-defined spatial direction. For temporal derivation, the prediction block is obtained by an average of the prediction blocks indicated by the motion vector prediction (MVP) and the scaled motion vector (MV) in a second reference. The second reference is specified by the reference index transmitted in the bitstream. For temporal multi-hypothesis prediction, as shown in Fig.