TRANSFORM DOMAIN SLICE BASED DISTRIBUTED VIDEO CODING

Distributed video coding depends heavily on the virtual channel model. Due to the limitations of the side information estimation one stationary model does not properly describe the virtual channel. In this work the correlation noise is modelled per slice to obtain location-specific correlation noise model. The resulting delay from the lengthy Slepian-Wolf (SW) codec input is also reduced by reducing the length of SW codec input. The proposed solution does not impose any extra complexity, it utilizes the existing resources. The results presented here support the proposed algorithm

BEST FIT MODELS TEST FOR THE VIRTUAL CHANNEL IN DISTRIBUTED VIDEO CODING

Wyner-Ziv (WZ) video coding – a particular case of distributed video coding (DVC) – is a new video coding paradigm based on two major Information Theory results: the Slepian-Wolf and Wyner-Ziv theorems. Most of the solutions available in the literature, model the correlation noise between the original frame and the so-called side information by virtual channel. However most of the DVC solutions in the literature assume Laplacian distribution as noise virtual channel model, in this study we perform three goodness-of-fit tests, the Kolmogorov-Smirnov test and the Chi-Square test and log-Likelihood test to study the nature of the virtual channel. The results show that a mixture of 3 (or 4) mixture Gaussian model can best describe this virtual channel