  We present a comprehensive study and evaluation of existing single image
compression artifacts removal algorithms, using a new 4K resolution benchmark
including diversified foreground objects and background scenes with rich
structures, called Large-scale Ideal Ultra high definition 4K (LIU4K)
benchmark. Compression artifacts 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, including architectures,
multi-domain sources, signal structures, and new targeted units, are
summarized. Furthermore, based on a unified deep learning configuration (i.e.
same training data, loss function, optimization algorithm, etc.), we evaluate
recent deep learning-based methods based on diversified evaluation measures.
The experimental results show the state-of-the-art performance comparison of
existing methods based on both full-reference, non-reference and task-driven
metrics. Our survey would give a comprehensive reference source for future
research on single image compression artifacts removal and inspire new
directions of the related fields.
