Fast depth decision for HEVC inter prediction based on spatial and temporal correlation

Gaoxing Chen; Zhenyu Liu; Takeshi Ikenaga

doi:10.1117/12.2243812

11 July 2016 Fast depth decision for HEVC inter prediction based on spatial and temporal correlation

Gaoxing Chen, Zhenyu Liu, Takeshi Ikenaga

Proceedings Volume 10011, First International Workshop on Pattern Recognition; 100110H (2016) https://doi.org/10.1117/12.2243812
Event: First International Workshop on Pattern Recognition, 2016, Tokyo, Japan

Abstract

High efficiency video coding (HEVC) is a video compression standard that outperforms the predecessor H.264/AVC by doubling the compression efficiency. To enhance the compression accuracy, the partition sizes ranging is from 4x4 to 64x64 in HEVC. However, the manifold partition sizes dramatically increase the encoding complexity. This paper proposes a fast depth decision based on spatial and temporal correlation. Spatial correlation utilize the code tree unit (CTU) Splitting information and temporal correlation utilize the motion vector predictor represented CTU in inter prediction to determine the maximum depth in each CTU. Experimental results show that the proposed method saves about 29.1% of the original processing time with 0.9% of BD-bitrate increase on average.

Citation Download Citation

Gaoxing Chen, Zhenyu Liu, and Takeshi Ikenaga "Fast depth decision for HEVC inter prediction based on spatial and temporal correlation", Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100110H (11 July 2016); https://doi.org/10.1117/12.2243812

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available