Content Based Fast Intra Coding for AVS2

Junru LI, Falei LUO, Yun ZHOU, Shanshe WANG, Meng WANG, Siwei MA

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

8 Citations (Scopus)

Abstract

AVS2 is the new generation of video coding standard developed by the Audio Video Coding Standard Working Group of China. In analogies to HEVC/H.265, a more flexible CU (coding unit), PU (prediction unit) and TU (transform unit) based coding structure is adopted to represent and organize the coding data. For intra coding, AVS2 adopts quad tree based partition coding structure on CU level, and one CU can be alternatively split into four PUs, which is known as short distance intra prediction (SDIP). SDIP can improve the coding performance but bring in great complexity. In this paper, we propose a content based fast intra coding optimization algorithm to reduce the encoding time for AVS2 all intra coding. Firstly, the content flatness (CF) of the coding block is computed. Based on the achieved content flatness, whether to split the current coding block into the next coding depth and whether to perform SDIP can be adaptively determined. Secondly, the rough mode set (RMS) is adaptively selected according to the achieved CF. Thus the number of mode for rate distortion optimization (RDO) can be significantly reduced. Experimental results show that the proposed fast algorithm can achieve over 43% complexity reduction on average under all intra testing configuration, while the average efficiency loss is negligible.

Original languageEnglish
Title of host publicationProceedings : 2017 IEEE 3rd International Conference on Multimedia Big Data, BigMM 2017
PublisherIEEE
Pages94-97
Number of pages4
ISBN (Electronic)9781509065493
DOIs
Publication statusPublished - 30 Jun 2017
Externally publishedYes
Event3rd IEEE International Conference on Multimedia Big Data, BigMM 2017 - Laguna Hills, United States
Duration: 19 Apr 201721 Apr 2017

Conference

Conference3rd IEEE International Conference on Multimedia Big Data, BigMM 2017
Country/TerritoryUnited States
CityLaguna Hills
Period19/04/1721/04/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Funding

ACKNOWLEDGMENT This work was supported in part by the National High-tech R&D Program of China (863 Program, 2015AA015903), National Natural Science Foundation of China (61322106, 61571017, 61421062), Shenzhen Peacock Plan, which are gratefully acknowledged.

Keywords

  • AVS2
  • content flatness
  • CU splitting
  • intra prediction
  • mode decision

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