# A Comprehensive Benchmark for Single Image Compression Artifacts Reduction

arXiv: https://arxiv.org/abs/1909.03647 · HTML: https://mlsanity.com/paper/1909.03647/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2019.

## Built on by (most cited first, 30 of 33)

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- [LLIC: Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression](https://mlsanity.com/paper/2304.09571/) (2023)
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- [Sensitivity Decouple Learning for Image Compression Artifacts Reduction](https://mlsanity.com/paper/2405.09291/) (2024)
- [JPEG AI Image Compression Visual Artifacts: Detection Methods and Dataset](https://mlsanity.com/paper/2411.06810/) (2024)
- [Picking Up Quantization Steps for Compressed Image Classification](https://mlsanity.com/paper/2304.10714/) (2023)
- [3DM-WeConvene: Learned Image Compression with 3D Multi-Level Wavelet-Domain Convolution and Entropy Model](https://mlsanity.com/paper/2504.04658/) (2025)
- [Efficient High-Resolution Image Editing with Hallucination-Aware Loss and Adaptive Tiling](https://mlsanity.com/paper/2510.06295/) (2025)
- [CISRNet: Compressed Image Super-Resolution Network](https://mlsanity.com/paper/2201.06045/) (2022)
- [Image Super-Resolution with Guarantees via Conformalized Generative Models](https://mlsanity.com/paper/2502.09664/) (2025)
- [FEDS: Feature and Entropy-Based Distillation Strategy for Efficient Learned Image Compression](https://mlsanity.com/paper/2503.06399/) (2025)
- [Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution Enhancement](https://mlsanity.com/paper/2103.01698/) (2021)
- [Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering](https://mlsanity.com/paper/2206.10618/) (2022)
- [Compact Latent Representation for Image Compression (CLRIC)](https://mlsanity.com/paper/2502.14937/) (2025)
- [LoC-LIC: Low Complexity Learned Image Coding Using Hierarchical Feature Transforms](https://mlsanity.com/paper/2504.21778/) (2025)
- [Addressing the Depth-of-Field Constraint: A New Paradigm for High Resolution Multi-Focus Image Fusion](https://mlsanity.com/paper/2510.19581/) (2025)
- [Blind Quality Enhancement of Compressed Video via Fine-Grained Degradation-Guided Sequential Inference](https://mlsanity.com/paper/2511.16137/) (2025)
- [A new Image Similarity Metric for a Perceptual and Transparent Geometric and Chromatic Assessment](https://mlsanity.com/paper/2601.19680/) (2026)

## Abstract (arXiv)

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.