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 andWZ 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.