Reducing Image Compression Artifacts by Structural Sparse Representation and Quantization Constraint Prior

The block discrete cosine transform (BDCT) has been widely used in current image and video coding standards, owing to its good energy compaction and decorrelation properties. However, because of independent quantization of DCT coefficients in each block, BDCT usually gives rise to visually annoying blocking compression artifacts, especially at low bit rates. In this paper, to reduce blocking artifacts and obtain high-quality images, image deblocking is cast as an optimization problem within maximum a posteriori framework, and a novel algorithm for image deblocking by using structural sparse representation (SSR) prior and quantization constraint (QC) prior is proposed. The SSR prior is utilized to simultaneously enforce the intrinsic local sparsity and the nonlocal self-similarity of natural images, while QC is explicitly incorporated to ensure a more reliable and robust estimation. A new split Bregman iteration-based method with an adaptively adjusted regularization parameter is developed to solve the proposed optimization problem, which makes the entire algorithm more practical. Experiments demonstrate that the proposed image-deblocking algorithm combining SSR and QC outperforms the current state-of-the-art methods in both peak signal-to-noise ratio and visual perception.