Visual Data Deblocking using Structural Layer Priors
This addresses visual quality issues in compressed media for computer vision applications, but it is incremental as it builds on existing layer separation concepts.
The paper tackled the problem of blocking artifacts in compressed images/videos by proposing a method to separate intrinsic and artifact layers using structural priors, achieving superior performance over state-of-the-art methods in visual quality and simplicity.
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.