Adaptive Perceptual Block Compressive Sensing for Image Compression

Abstract

SUMMARYBecause the perceptual compressive sensing framework can achieve a much better performance than the legacy compressive sensing framework, it is very promising for the compressive sensing based image compression system. In this paper, we propose an innovative adaptive perceptual block compressive sensing scheme. Firstly, a new block-based statistical metric which can more appropriately measure each block's sparsity and perceptual sensibility is devised. Then, the approximated theoretical minimum measurement number for each block is derived from the new block-based metric and used as weight for adaptive measurements allocation. The obtained experimental results show that our scheme can significantly enhance both objective and subjective performance of a perceptual compressive sensing framework. key words: perceptual compressive sensing, adaptive measurements allocation, discrete cosine transform, image compression

Adaptive gradient-based block compressive sensing with sparsity for noisy images

“…They used certain block partitions and the block size ranged from 4 to 90. Both [42] and [48] proposed an adaptive block-based compressive sensing approach which collected a different number of samples of the measurement matrix for each block. [9] studied block compressive sensing in wireless sensor networks and [3] analyzed the block sampling strategies in compressive sensing.…”

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“…In order to obtain high performances, original signals are required to be sufficient sparse in some special transformations. Natural images usually exhibit local regularities and global symmetries; their signals can be represented sparsely by the overcompleted dictionary, which provide a base for image compression [3][4][5]. CS has some problems such as higher computation complexity, lower efficiency, less controllable process and greater difficulty obtaining an appropriate dictionary.…”