Reduction of blocking artifacts in image and video coding
The discrete cosine transform (DCT) is the most popular transform for image and video compression. Many international standards such as JPEG, MPEG, and H.261 are based on a block-DCT scheme. High compression ratios are obtained by discarding information about DCT coefficients that is considered to be less important. The major drawback is visible discontinuities along block boundaries, commonly referred to as blocking artifacts. These often limit the maximum compression ratios that can be achieved. Various postprocessing techniques have been published that reduce these blocking effects, but most of them introduce unnecessary blurring, ringing, or other artifacts. In this paper, a novel postprocessing algorithm based on Markov random fields (MRFs) is proposed. It efficiently removes blocking effects while retaining the sharpness of the image and without introducing new artifacts. The degraded image is first segmented into regions, and then each region is enhanced separately to prevent blurring of dominant edges. A novel texture detector allows the segmentation of images containing both texture and monotone areas. It finds all texture regions in the image before the remaining monotone areas are segmented by an MRF segmentation algorithm that has a new edge component incorporated to detect dominant edges more reliably. The proposed enhancement stage then finds the maximum a posteriori estimate of the unknown original image, which is modeled by an MRF and is therefore Gibbs distributed. A very efficient implementation is presented. Experiments demonstrate that our proposed postprocessor gives excellent results compared to other approaches, from both a subjective and an objective viewpoint. Furthermore, it will be shown that our technique also works for wavelet encoded images, which typically contain ringing artifacts.