Image decoding optimization based on compressive sensing |
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Authors: | Zhen ZhangYunhui Shi Dehui KongWenpeng Ding Baocai Yin |
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Institution: | Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Beijing University of Technology, Beijing 100124, China |
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Abstract: | Transform-based image codec follows the basic principle: the reconstructed quality is decided by the quantization level. Compressive sensing (CS) breaks the limit and states that sparse signals can be perfectly recovered from incomplete or even corrupted information by solving convex optimization. Under the same acquisition of images, if images are represented sparsely enough, they can be reconstructed more accurately by CS recovery than inverse transform. So, in this paper, we utilize a modified TV operator to enhance image sparse representation and reconstruction accuracy, and we acquire image information from transform coefficients corrupted by quantization noise. We can reconstruct the images by CS recovery instead of inverse transform. A CS-based JPEG decoding scheme is obtained and experimental results demonstrate that the proposed methods significantly improve the PSNR and visual quality of reconstructed images compared with original JPEG decoder. |
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Keywords: | Compressive sensing Image decoding Total Variation SOCP |
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