A Comprehensive Benchmark for Single Image Compression Artifact Reduction

Jiaying Liu, Dong Liu, Wenhan Yang, Sifeng Xia, Xiaoshuai Zhang, Yuanying Dai

Abstract: We present a comprehensive study and evaluation of existing single image compression artifact removal algorithms using a new 4K resolution benchmark. This benchmark is called the Large-Scale Ideal Ultra high-definition 4K (LIU4K), and it includes including diversified foreground objects and background scenes with rich structures. Compression artifact removal, as a common post-processing technique, aims at alleviating undesirable artifacts, such as blockiness, ringing, and banding caused by quantization and approximation in the compression process. In this work, a systematic listing of the reviewed methods is presented based on their basic models (handcrafted models and deep networks). The main contributions and novelties of these methods are highlighted, and the main development directions are summarized, including architectures, multi-domain sources, signal structures, and new targeted units. Furthermore, based on a unified deep learning configuration ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> same training data, loss function, optimization algorithm, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc.</i> ), we evaluate recent deep learning-based methods based on diversified evaluation measures. The experimental results show state-of-the-art performance comparisons of existing methods based on both full-reference, non-reference, and task-driven metrics. Our survey gives a comprehensive reference source for future research on single image compression artifact removal and inspires new directions in related fields.