Adaptive correlation estimation for general wyner-ZIV video coding

Wyner-Ziv video coding (WZVC) is a new paradigm for video compression with the prediction frames possibly only available at the decoder. It exploits the statistics between the source frame and the prediction frame at the decoder by utilizing their correlation information. This correlation information is important but also difficult to estimate due to the absolute absence of the prediction frame at the encoder, and the lack of the source frame at the decoder. In this paper, we focus on this issue and derive a coefficient-level adaptive correlation model for general Wyner-Ziv video coding. Based on this model, we propose an online transform-domain adaptive correlation estimation (TRACE) approach, in which the correlation information is progressively learned during the decoding process. In our experiments, the proposed approach outperforms the existing approaches up to 4dB. More importantly, different from existing coefficient-level variance estimation approaches, the proposed online TRACE is applicable for not only low complexity WZVC but also other WZVCs such as flexible WZVC as demonstrated in the experiments.