Publication Details
Overview
 
 
Nikos Deligiannis, Joeri Barbarien, Marc Jacobs, Adrian Munteanu, Athanassios Skodras, Peter Schelkens
 

IEEE Transactions on Image Processing

Contribution To Journal

Abstract 

In the context of low-cost video encoding, distributed video coding (DVC) has recently emerged as a potential candidate for uplink-oriented applications. This paper builds on a correlation channel modeling concept which expresses the correlation noise as being statistically dependent on the side-information. Compared to classical side-information independent noise model-ing adopted in current DVC solutions, it is theoretically proven that side-information dependent modeling improves the Wyner-Ziv coding performance. Anchored in this finding, this paper proposes a novel algorithm for online estimation of the side-information dependent correlation channel parameters based on already decoded information. The proposed algorithm enables bit-plane-by-bit-plane successive refinement of the channel esti-mation leading to progressively improved accuracy. Additionally, the proposed algorithm is included in a novel DVC architecture which employs a competitive hash-based motion estimation tech-nique to generate high quality side-information at the decoder. Experimental results corroborate our theoretical gains and vali-date the accuracy of the channel estimation algorithm. The per-formance assessment of the proposed architecture shows remark-able and consistent coding gains over a germane group of state-of-the-art distributed and standard video codecs, even under strenuous conditions, that is, large groups of pictures (GOP) and highly irregular motion content.

Reference