Correlation noise classification based on matching success for transform domain Wyner-Ziv video coding

Distributed source coding strongly depends on the knowledge of statistical dependency between source and side information. In transform domain Wyner-Ziv video coding (TDWZ) this statistical dependency (also known as correlation noise) has been usually modeled by a unique Laplacian distribution for each frequency band. In this paper, we propose a method to define different classes of correlation noise for each frequency band based on the accuracy of the side information. With this approach the correlation between source and side information is estimated separately for each frequency band of each class. Therefore, the decoder can discriminate blocks in order to estimate the correlation noise of their frequency bands. Simulation results show that applying the proposed method improves rate-distortion performance.