Compression Artifacts Reduction by a Deep Convolutional Network

TL;DR

This paper introduces a deep convolutional network designed to effectively reduce various compression artifacts in images, outperforming existing methods on benchmarks and real-world data, and can also serve as a pre-processing step for other vision tasks.

Contribution

The paper proposes a compact, efficient deep convolutional network for artifact reduction that leverages transfer learning and demonstrates superior performance over state-of-the-art methods.

Findings

Outperforms existing artifact reduction algorithms on benchmarks.

Effective transfer learning improves low-level vision tasks.

Applicable as pre-processing for other vision routines.

Abstract

Lossy compression introduces complex compression artifacts, particularly the blocking artifacts, ringing effects and blurring. Existing algorithms either focus on removing blocking artifacts and produce blurred output, or restores sharpened images that are accompanied with ringing effects. Inspired by the deep convolutional networks (DCN) on super-resolution, we formulate a compact and efficient network for seamless attenuation of different compression artifacts. We also demonstrate that a deeper model can be effectively trained with the features learned in a shallow network. Following a similar "easy to hard" idea, we systematically investigate several practical transfer settings and show the effectiveness of transfer learning in low-level vision problems. Our method shows superior performance than the state-of-the-arts both on the benchmark datasets and the real-world use case (i.e. Twitter). In addition, we show that our method can be applied as pre-processing to facilitate other low-level vision routines when they take compressed images as input.