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Sub-Sampling Framework of Distributed Video Coding

Distributed video coding (DVC) has recently been proposed to reduce the complexity of the encoder, whereas it suffers from the sampling cost of huge amount of image data. To relax such sampling burden, this paper develops a novel sub-sampling distributed video coding (SuDVC) by utilizing compressive sensing (CS) technique. Due to the inherent sparsity in video sources, the video frames are compressively sampled at the encoder. On the other hand, by exploiting the correlation between CS measurements and side information and by performing sparsity recovery, the video frames are recovered at the decoder. When compared with the traditional fully-sampling equivalence, SuDVC enjoys the reduction of transmission rate, the reduction of implementation complexity and the robustness to channel losses, which are verified in the simulations.