Variational JPEG artifacts suppression based on high-order MRFs

The Block-wise Discrete Cosine Transform (B-DCT) based compression technique has been widely used in image and video coding standards. However, at high compression ratios, the coded images inevitably contain annoying blocking artifacts. In this paper, the author proposes a novel variational model for blocking artifacts suppression, which combines a discriminatively trained Fields of Experts (FoE) image prior model and the indicator function of the quantization constraint set (QCS). The FoE prior model is a filter-based higher-order Markov Random Fields (MRF) model, and it has proven to be effective for many image restoration problems. The resulting variational model leads to a generally difficult non-convex optimization problem, which can be efficiently solved by a recently proposed non-convex optimization algorithm. Numerical experiments show that the proposed deblocking approach leads to visually strongly comparable performance to state-of-the-art deblocking methods across a range of compression levels. Furthermore, our method can achieve higher PSNR-B results, which is a block-sensitive index, specialized for deblocked image evaluation and correlates well with subjective quality. Besides, the proposed model comes along with the additional advantage of high efficiency.