Image deblocking using joint Gaussian mixture model and anchored neighborhood regression priors

Abstract

At low bit rates, Block based transform coding method uses large quantization step to quantize transform coefficients, which usually causes compression artifacts for images. Post-processing strategy is a promising solution which can greatly improve the visual quality of degraded images without change of existing codec. In this paper, we propose an image deblocking method for JPEG compressed images using joint Gaussian mixture model (GMM) and anchored neighborhood regression priors. The proposed method takes advantage of image priors to reduce blocking artifacts and achieve a better image quality simultaneously. First, we utilize GMM to reduce blocking artifacts. Based on the assumption that similar image patches can be derived from one certain Gaussian probability distribution, we formulate the image deblocking as an optimization problem by maximizing a posteriori function. Solving this problem ultimately boils down to the liner Wiener filtering. We then learn mapping functions offline based on the recent adjusted anchored neighborhood regression to enhance image details and edges. Extensive experimental results validate that our proposed method performs better both objectively and subjectively compared to some recently presented methods.