Please use this identifier to cite or link to this item:
|Title:||Efficient temporal and interlayer parameter prediction for weighted prediction in scalable high efficiency video coding||Author(s):||Siu, Wan Chi||Author(s):||Tsang, S.-H.
|Issue Date:||2017||Publisher:||Society of Photo-Optical Instrumentation Engineers (SPIE); Society for Imaging Science and Technology (IS&T)||Journal:||Journal of Electronic Imaging||Volume:||26||Issue:||1||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.
|URI:||https://repository.cihe.edu.hk/jspui/handle/cihe/2281||DOI:||10.1117/1.JEI.26.1.013013||CIHE Affiliated Publication:||No|
|Appears in Collections:||CIS Publication|
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.