Abstract
Wyner-Ziv (WZ) video coding is a particular case of distributed video coding, which is a recent video coding paradigm based on the Slepian-Wolf and WZ theorems. Contrary to available prediction-based standard video codecs, WZ video coding exploits the source statistics at the decoder, allowing the development of simpler encoders. Until now, WZ video coding did not reach the compression efficiency performance of conventional video coding solutions, mainly due to the poor quality of the side information, which is an estimate of the original frame created at the decoder in the most popular WZ video codecs. In this context, this paper proposes a novel side information refinement (SIR) algorithm for a transform domain WZ video codec based on a learning approach where the side information is successively improved as the decoding proceeds. The results show significant and consistent performance improvements regarding state-of-the-art WZ and standard video codecs, especially under critical conditions such as high motion content and long group of pictures sizes.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Circuits and Systems for Video Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
