Image deblocking using group-based sparse representation and quantization constraint prior

To alleviate the conflict between bit reduction and quality preservation, deblocking as a post-processing strategy is an attractive and promising solution without changing existing codec. In this paper, in order to reduce blocking artifacts and obtain high-quality image, image deblocking is formulated as an optimization problem via maximum a posteriori framework, and a novel algorithm for image deblocking using group-based sparse representation (GSR) and quantization constraint (QC) prior is proposed. GSR prior is utilized to simultaneously enforce the intrinsic local sparsity and the nonlocal self-similarity of natural images, while QC prior is explicitly incorporated to ensure a more reliable and robust estimation. A new split Bregman iteration based method with adaptively adjusted regularization parameter is developed to solve the proposed optimization problem for image deblocking. The parameter-adaptive advantage enables the whole algorithm more attractive and practical. Experiments manifest that the proposed image deblocking algorithm improves current state-of-the-art results by a large margin in both PSNR and visual perception.