Regularized dequantization for image copies compressed with different quantization parameters
A single source image can be compressed into many different copies with different quantization parameters. An end user may have access to a few of such copies (e.g., over the Internet), but would like to obtain one that has superior quality to any available copy. We propose in this paper to improve the reconstruction quality by using regularization and refining the narrowed quantization constraint set (NQCS) from such image copies. Since the size of NQCS is generally large, we also discuss the estimation of initial images for regularization by applying proper probability models. Experimental results show that the proposed scheme can consistently improve the quality of the reconstructed images in both subjective and objective terms.