A Comprehensive Benchmark for Single Image Compression Artifacts Reduction

Abstract

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.

https://flyywh.github.io/LIU4K_Website/

References in corpus (11)

• Adam: A Method for Stochastic Optimization
• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration
• Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
• A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding
• Multi-Frame Quality Enhancement for Compressed Video
• MemNet: A Persistent Memory Network for Image Restoration
• CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression
• Enhancing HEVC Compressed Videos with a Partition-masked Convolutional Neural Network
• Deep Convolution Networks for Compression Artifacts Reduction
• A practical convolutional neural network as loop filter for intra frame

Cited by in corpus (6)

• HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
• MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression
• LLIC: Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression
• Picking Up Quantization Steps for Compressed Image Classification
• Learning End-to-End Lossy Image Compression: A Benchmark
• Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution Enhancement