Adaptive Sampling Rate Allocation Based on Image Entropy for Block-Based Compressed Sensing of Video
Abstract
The traditional block-based compressed sensing (BCS) of video is measured at a fixed sampling rate. In these schemes, when the video is reconstructed, the block effect occurs due to the spatial redundancy of the image. To solve this problem, we proposed the image entropy as allocation condition of sampling rate for each block. This method calculates the entropy of the difference between the key frames and the non-key frames. Simulation results show that the proposed method has superior performance of the reconstructed video and subjective vision in comparison to the adaptive scheme based on variance and the time of reconstruction is also reduced.
Keywords
distributed video, Compressed sensing (CS), Adaptive sampling, Image entropy
DOI
10.12783/dtcse/cmsam2017/16431
10.12783/dtcse/cmsam2017/16431
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