Multi-hypothesis compressed video sensing by two-step iterative thresholding

Abstract

Traditional distributed video coding schemes achieved compression by using a single reference frame for side information, which plays a key-role in determining the performance of the codec. To refine side information, we propose a novel side information generation algorithm by using two neighbouring key frames as reference. The proposed algorithm uses multi-hypothesis predictions to fully utilise the intra and inter frame correlations. Three side information candidates are obtained by applying multi-hypothesis predictions and bi-directional motion estimation on the non-key frame and its bi-directional reference frames. The most similar candidate is chosen by calculating the correlation coefficients between the non-key frame and the three candidates. With the refined side information, the video frames are reconstructed by blocked compressed sensing with a smoothed project-landweber (BCS-SPL) algorithm. Experimental results demonstrate that the proposed algorithm outperforms the original schemes in refining the side information as well as improving the reconstruction performance.