Efficient temporal and interlayer parameter prediction for weighted prediction in scalable high efficiency video coding

Sik-Ho Tsang; Yui-Lam Chan; Wan-Chi Siu

doi:10.1117/1.JEI.26.1.013013

11 February 2017 Efficient temporal and interlayer parameter prediction for weighted prediction in scalable high efficiency video coding

Sik-Ho Tsang, Yui-Lam Chan, Wan-Chi Siu

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 1, 013013 (February 2017). https://doi.org/10.1117/1.JEI.26.1.013013

Abstract

Weighted prediction (WP) is an efficient video coding tool that was introduced since the establishment of the H.264/AVC video coding standard, for compensating the temporal illumination change in motion estimation and compensation. WP parameters, including a multiplicative weight and an additive offset for each reference frame, are required to be estimated and transmitted to the decoder by slice header. These parameters cause extra bits in the coded video bitstream. High efficiency video coding (HEVC) provides WP parameter prediction to reduce the overhead. Therefore, WP parameter prediction is crucial to research works or applications, which are related to WP. Prior art has been suggested to further improve the WP parameter prediction by implicit prediction of image characteristics and derivation of parameters. By exploiting both temporal and interlayer redundancies, we propose three WP parameter prediction algorithms, enhanced implicit WP parameter, enhanced direct WP parameter derivation, and interlayer WP parameter, to further improve the coding efficiency of HEVC. Results show that our proposed algorithms can achieve up to 5.83% and 5.23% bitrate reduction compared to the conventional scalable HEVC in the base layer for SNR scalability and 2× spatial scalability, respectively.

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Sik-Ho Tsang, Yui-Lam Chan, and Wan-Chi Siu "Efficient temporal and interlayer parameter prediction for weighted prediction in scalable high efficiency video coding," Journal of Electronic Imaging 26(1), 013013 (11 February 2017). https://doi.org/10.1117/1.JEI.26.1.013013

Received: 24 August 2016; Accepted: 17 January 2017; Published: 11 February 2017

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