Cross-band noise model refinement for transform domain Wyner–Ziv video coding

Distributed Video Coding (DVC) is a new video coding paradigm, which mainly exploits the source statistics at the decoder based on the availability of decoder side information. One approach to DVC is feedback channel based Transform Domain Wyner–Ziv (TDWZ) video coding. The efficiency of current TDWZ video coding trails that of conventional video coding solutions, mainly due to the quality of side information, inaccurate noise modeling and loss in the final coding step. The major goal of this paper is to enhance the accuracy of the noise modeling, which is one of the most important aspects influencing the coding performance of DVC. A TDWZ video decoder with a novel cross-band based adaptive noise model is proposed, and a noise residue refinement scheme is introduced to successively update the estimated noise residue for noise modeling after each bit-plane. Experimental results show that the proposed noise model and noise residue refinement scheme can improve the rate-distortion (RD) performance of TDWZ video coding significantly. The quality of the side information modeling is also evaluated by a measure of the ideal code length.

► Cross-band correlation and decoded information are used to improve noise model.
► Coefficients are classified and adaptively assigned with a more accurate parameter.
► Noise residue is iteratively refined after each bit-plane is decoded.
► Significant RD improvements are robustly achieved for the set of test sequences.
► Potential performance of DVC with ideal code length is also reported.