NDLI: Efficient weighted prediction parameter signaling using parameter prediction and direct derivation

Content Provider	IEEE Xplore Digital Library
Author	Tanizawa, A. Chujoh, T.
Copyright Year	2013
Abstract	Weighted prediction (WP) is an efficient motion compensation technique to predict temporal illumination variation. It has been introduced in H.264/MPEG-4 AVC (Advanced Video Coding, H.264) and consists of two modes: an explicit WP (EWP) and an implicit WP (IWP). Since in EWP, a weighting factor and an offset for each reference picture are explicitly transmitted to the decoder, an overhead of these parameters affects the coding efficiency. Since in H.264-based EWP, these parameters are directly encoded without prediction, it is difficult to apply WP for low bitrate applications. In order to reduce the overhead of them, this paper presents an efficient weighted prediction parameter signaling method towards High Efficiency Video Coding (HEVC) standard by introducing a WP parameter prediction and a direct WP parameter derivation. A weighting factor and an offset are predicted using the characteristics of WP parameters and entropy-coded. Moreover, when there are the same entries in reference pictures for prediction list 0 and 1 in the case of B-slices, a set of WP parameters for prediction list 1 is directly derived from that for prediction list 0. The experimental results show that our method can achieve up to 66% overhead bitrate reduction and up to 7% overall bitrate reduction compared to the conventional H.264-based EWP.
Starting Page	1900
Ending Page	1903
File Size	1894060
Page Count	4
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738391
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	HEVC Video coding Weighted prediction Explicit mode Motion compensation
Content Type	Text
Resource Type	Article

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