Visual Data Deblocking using Structural Layer Priors

TL;DR

This paper introduces a robust method for removing blocking artifacts from compressed images and videos by separating intrinsic and artifact layers using structural priors and an efficient optimization algorithm.

Contribution

It proposes a novel layer separation approach leveraging gradient sparsity and independence, with an Augmented Lagrangian Multiplier algorithm for improved artifact removal.

Findings

Outperforms state-of-the-art methods in visual quality

Efficiently separates intrinsic and artifact layers

Demonstrates robustness across various compressed images and videos

Abstract

The blocking artifact frequently appears in compressed real-world images or video sequences, especially coded at low bit rates, which is visually annoying and likely hurts the performance of many computer vision algorithms. A compressed frame can be viewed as the superimposition of an intrinsic layer and an artifact one. Recovering the two layers from such frames seems to be a severely ill-posed problem since the number of unknowns to recover is twice as many as the given measurements. In this paper, we propose a simple and robust method to separate these two layers, which exploits structural layer priors including the gradient sparsity of the intrinsic layer, and the independence of the gradient fields of the two layers. A novel Augmented Lagrangian Multiplier based algorithm is designed to efficiently and effectively solve the recovery problem. Extensive experimental results demonstrate the superior performance of our method over the state of the arts, in terms of visual quality and simplicity.