Block artifact reduction using a transform-domain Markov random field model

The block-based discrete cosine transform (BDCT) is often used in image and video coding. It may introduce block artifacts at low data rates that manifest themselves as an annoying discontinuity between adjacent blocks. In this paper, we address this problem by investigating a transform-domain Markov random field (TD-MRF) model. Based on this model, two block artifact reduction postprocessing methods are presented. The first method, referred to as TD-MRF, provides an efficient progressive transform-domain solution. Our experimental results show that TD-MRF can reduce up to 90% of the computational complexity compared with spatial-domain MRF (SD-MRF) methods while still achieving comparable visual quality improvements. We then discuss a hybrid framework, referred to as TSD-MRF, that exploits the advantages of both TD-MRF and SD-MRF. The experimental results confirm that TSD-MRF can improve visual quality both objectively and subjectively over SD-MRF methods.

基于块的离散余弦变换（BDCT）常用于图像和视频编码。在低数据率下，它可能会引入块效应，表现为相邻块之间令人讨厌的不连续性。在本文中，我们通过研究一种变换域马尔可夫随机场（TD - MRF）模型来解决这个问题。基于该模型，提出了两种减少块效应的后处理方法。第一种方法称为TD - MRF，它提供了一种高效的渐进变换域解决方案。我们的实验结果表明，与空间域MRF（SD - MRF）方法相比，TD - MRF可将计算复杂度降低多达90%，同时仍能实现相当的视觉质量改善。然后我们讨论了一种混合框架，称为TSD - MRF，它利用了TD - MRF和SD - MRF两者的优势。实验结果证实，与SD - MRF方法相比，TSD - MRF在客观和主观上都能提高视觉质量。