# CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression

arXiv: https://arxiv.org/abs/1611.07233 · HTML: https://mlsanity.com/paper/1611.07233/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2016.

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

- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [MambaIR: A Simple Baseline for Image Restoration with State-Space Model](https://mlsanity.com/paper/2402.15648/) (2024)
- [DiffIR: Efficient Diffusion Model for Image Restoration](https://mlsanity.com/paper/2303.09472/) (2023)
- [Towards Flexible Blind JPEG Artifacts Removal](https://mlsanity.com/paper/2109.14573/) (2021)
- [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://mlsanity.com/paper/2209.11345/) (2022)
- [CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution](https://mlsanity.com/paper/2212.04362/) (2022)
- [Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling](https://mlsanity.com/paper/2503.06617/) (2025)
- [DiffI2I: Efficient Diffusion Model for Image-to-Image Translation](https://mlsanity.com/paper/2308.13767/) (2023)
- [Basic Binary Convolution Unit for Binarized Image Restoration Network](https://mlsanity.com/paper/2210.00405/) (2022)
- [HST: Hierarchical Swin Transformer for Compressed Image Super-resolution](https://mlsanity.com/paper/2208.09885/) (2022)
- [Deep Equilibrium Diffusion Restoration with Parallel Sampling](https://mlsanity.com/paper/2311.11600/) (2023)
- [Attribute Artifacts Removal for Geometry-based Point Cloud Compression](https://mlsanity.com/paper/2112.00560/) (2021)
- [S3Mamba: Arbitrary-Scale Super-Resolution via Scaleable State Space Model](https://mlsanity.com/paper/2411.11906/) (2024)
- [Functional Neural Networks for Parametric Image Restoration Problems](https://mlsanity.com/paper/2111.00361/) (2021)
- [A Dive into SAM Prior in Image Restoration](https://mlsanity.com/paper/2305.13620/) (2023)
- [High-Perceptual Quality JPEG Decoding via Posterior Sampling](https://mlsanity.com/paper/2211.11827/) (2022)
- [JDEC: JPEG Decoding via Enhanced Continuous Cosine Coefficients](https://mlsanity.com/paper/2404.05558/) (2024)
- [Perceptual Quality Improvement in Videoconferencing using Keyframes-based GAN](https://mlsanity.com/paper/2311.04263/) (2023)
- [HDCompression: Hybrid-Diffusion Image Compression for Ultra-Low Bitrates](https://mlsanity.com/paper/2502.07160/) (2025)
- [LKFormer: Large Kernel Transformer for Infrared Image Super-Resolution](https://mlsanity.com/paper/2401.11859/) (2024)
- [Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancement](https://mlsanity.com/paper/2202.00011/) (2022)
- [Image Generative Semantic Communication with Multi-Modal Similarity Estimation for Resource-Limited Networks](https://mlsanity.com/paper/2404.11280/) (2024)
- [AnySR: Realizing Image Super-Resolution as Any-Scale, Any-Resource](https://mlsanity.com/paper/2407.04241/) (2024)
- [OAPT: Offset-Aware Partition Transformer for Double JPEG Artifacts Removal](https://mlsanity.com/paper/2408.11480/) (2024)
- [Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration](https://mlsanity.com/paper/2412.03814/) (2024)
- [A Global Appearance and Local Coding Distortion based Fusion Framework for CNN based Filtering in Video Coding](https://mlsanity.com/paper/2106.12746/) (2021)
- [Alternative Data Augmentation for Industrial Monitoring using Adversarial Learning](https://mlsanity.com/paper/2205.04222/) (2022)
- [An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution](https://mlsanity.com/paper/2401.15613/) (2024)
- [Deep learning-based Edge-aware pre and post-processing methods for JPEG compressed images](https://mlsanity.com/paper/2104.04926/) (2021)
- [Neural JPEG: End-to-End Image Compression Leveraging a Standard JPEG Encoder-Decoder](https://mlsanity.com/paper/2201.11795/) (2022)

## Abstract (arXiv)

Lossy image compression algorithms are pervasively used to reduce the size of images transmitted over the web and recorded on data storage media. However, we pay for their high compression rate with visual artifacts degrading the user experience. Deep convolutional neural networks have become a widespread tool to address high-level computer vision tasks very successfully. Recently, they have found their way into the areas of low-level computer vision and image processing to solve regression problems mostly with relatively shallow networks. We present a novel 12-layer deep convolutional network for image compression artifact suppression with hierarchical skip connections and a multi-scale loss function. We achieve a boost of up to 1.79 dB in PSNR over ordinary JPEG and an improvement of up to 0.36 dB over the best previous ConvNet result. We show that a network trained for a specific quality factor (QF) is resilient to the QF used to compress the input image - a single network trained for QF 60 provides a PSNR gain of more than 1.5 dB over the wide QF range from 40 to 76.