Transform-Domain Adaptive Correlation Estimation (TRACE) for Wyner–Ziv Video Coding

Abstract

Wyner-Ziv video coding (WZVC) is a newly emerged video coding scheme which compresses the input video frames with the side information (SI) frames only available at the decoder. WZVC exploits the statistics between the source frame and the SI frame at the decoder by utilizing their correlation information. This correlation information is important but also difficult to estimate due to the absence of the SI frame at the encoder, and the lack of the source frame at the decoder. In this paper, we focus on this problem and propose a novel transform-domain adaptive correlation estimation method called TRACE for WZVC. In TRACE, the correlation information is progressively learned during the decoding process of each frame. Within TRACE, we also propose a convex optimization based band-level correlation estimation method which is optimal in the sense of minimizing the theoretical bit rate. Experiments suggest that, when applied in motion compensated interpolation-based low complexity WZVC, TRACE yields competitive results against the state-of-the-art correlation estimation algorithms. More importantly, different from the existing coefficient-level correlation estimation algorithms, the proposed TRACE can be applied in many other WZVC schemes and can provide considerable gain over the popular band-level correlation estimation methods.