In this paper, we propose a novel postprocessing technique for compression-artifact reduction. Our approach is based on posing this task as an inverse problem, with a regularization that leverages on existing state-of-the-art image denoising algorithms. We rely on the recently proposed Plugand-Play Prior framework, suggesting the solution of general inverse problems via alternating direction method of multipliers, leading to a sequence of Gaussian denoising steps. A key feature in our scheme is a linearization of the compression-decompression process, so as to get a formulation that can be optimized. In addition, we supply a thorough analysis of this linear approximation for several basic compression procedures. The proposed method is suitable for diverse compression techniques that rely on transform coding. In particular, we demonstrate impressive gains in image quality for several leading compression methods-JPEG, JPEG2000, and HEVC.
In particular, Dar et al. REF designed a sequential denoising-based soft decoding algorithm, where the existing state-of-the-art denoising method was used to construct a regularization.
Postprocessing of Compressed Images via Sequential Denoising
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Computer Science Mathematics Medicine