Relative Sparsity Estimation Based Compressive Sensing for Image Compression Applications

摘要: Compressive sensing (CS) is a new efficient framework for sparse signal acquisition, which has been widely used in many application fields, such as multimedia coding and processing, etc. In this paper, novel block-based compressive scheme robust image compression applications proposed, where the relative sparsity of chunks are exploited to effectively allocate resources different blocks. The split into non-overlapping blocks fixed size, independently represented by discrete cosine transform (DCT) domain. key idea assign more with rich edge texture features but less located at smooth regions. Simulation results standard test images demonstrate that proposed can get significant performance gain reducing measurement rate and/or enhancing reconstructed quality.