NDLI: Group-Based Fast Mode Decision Algorithm for Intra Prediction in HEVC

Content Provider	IEEE Xplore Digital Library
Author	Shunqing Yan Liang Hong Weifeng He Qin Wang
Copyright Year	2012
Abstract	High Efficiency Video Coding (HEVC), also known as H.265, is the next generation video compression standard beyond H.264. 35 intra prediction modes(IPM) are defined in HEVC to improve coding efficiency. But so many modes also result in the dramatically increased encoding complexity. In this paper, a novel fast intra mode decision algorithm is proposed to speed up the original intra coding in HEVC. Different from previous algorithms, we analyzed the statistics generated by rough mode decision (RMD) process which is adopted in HEVC. First, we found that the modes generated by RMD are usually adjacent to each other, thus representing roughly a same direction. We merged adjacent modes into same groups. Furthermore, early termination and pixel-based edge detection methods are employed to make accurate prediction by heuristically reducing the number of candidates for rate distortion optimization (RDO) process. Experimental results show that our proposed fast intra mode decision algorithm can obtain almost 23.52% time savings in all intra low complexity cases on average with negligible performance loss compared to the original HEVC intra coding in HM7.0. Meanwhile, the results are especially favorable when it comes to high resolution test sequences with up to 0.4% bit-rate increment and 21.81% time savings.
Starting Page	225
Ending Page	229
File Size	219680
Page Count	5
File Format	PDF
ISBN	9781467351522
DOI	10.1109/SITIS.2012.41
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Video coding intra prediction Image edge detection Prediction algorithms Encoding HEVC mode decision Computational complexity Standards
Content Type	Text
Resource Type	Article

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