Undecoded Coefficients Recovery in Distributed Video Coding by Exploiting Spatio-Temporal Correlation: A Linear Programming Approach

Distributed video coding (DVC) aims at achieving low-complexity encoding in contrast to the existing video coding standards' high complexity encoding. According to the Wyner-Ziv theorem this can be achieved, under certain conditions, by independent encoding of the frames while resorting to joint decoding. However, the performance of a Wyner-Ziv coding scheme significantly depends on its knowledge about the spatio-temporal correlation of the video. Unfortunately, correlation statistics in a video widely varies both along the spatial and temporal directions. Therefore, we argue that in a feedback free transform domain DVC scheme the decoder will fail to recover all the transform coefficients with a nonzero probability. Thus, we suggest to integrate a recovery method with the decoder that aims at recovering the undecoded coefficients by exploiting the spatio-temporal correlation of the video. Besides, we extend and modify a recovery scheme, recently proposed in the context of images, for DVC so that it exploits both spatial and temporal correlations in recovering the undecoded coefficients. The essential idea of this scheme is to formulate the recovery problem as a linear optimization problem which can be solved efficiently using linear programming. Our simulation results demonstrated that the proposed scheme can significantly improve the PSNR and visual quality of the erroneous video frames produced by a DVC decoder.