# COOL-CHIC: Coordinate-based Low Complexity Hierarchical Image Codec

arXiv: https://arxiv.org/abs/2212.05458 · HTML: https://mlsanity.com/paper/2212.05458/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2022.

## Built on by (most cited first, 30 of 51)

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- [RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations](https://mlsanity.com/paper/2309.17182/) (2023)
- [In Search of a Data Transformation That Accelerates Neural Field Training](https://mlsanity.com/paper/2311.17094/) (2023)
- [Scene Matters: Model-based Deep Video Compression](https://mlsanity.com/paper/2303.04557/) (2023)
- [Content-Aware Mamba for Learned Image Compression](https://mlsanity.com/paper/2508.02192/) (2025)
- [GaussianImage++: Boosted Image Representation and Compression with 2D Gaussian Splatting](https://mlsanity.com/paper/2512.19108/) (2025)
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- [JPEG AI Image Compression Visual Artifacts: Detection Methods and Dataset](https://mlsanity.com/paper/2411.06810/) (2024)
- [Hybrid Neural Representations for Spherical Data](https://mlsanity.com/paper/2402.05965/) (2024)
- [Neural Video Representation for Redundancy Reduction and Consistency Preservation](https://mlsanity.com/paper/2409.18497/) (2024)
- [UAR-NVC: A Unified AutoRegressive Framework for Memory-Efficient Neural Video Compression](https://mlsanity.com/paper/2503.02733/) (2025)
- [How to Design and Train Your Implicit Neural Representation for Video Compression](https://mlsanity.com/paper/2506.24127/) (2025)
- [Enhanced Neural Video Representation Compression Across Extreme Complexity and Quality Scales](https://mlsanity.com/paper/2606.28163/) (2026)
- [An Efficient Implicit Neural Representation Image Codec Based on Mixed Autoregressive Model for Low-Complexity Decoding](https://mlsanity.com/paper/2401.12587/) (2024)
- [Survey on Visual Signal Coding and Processing with Generative Models: Technologies, Standards and Optimization](https://mlsanity.com/paper/2405.14221/) (2024)
- [Unicorn: Unified Neural Image Compression with One Number Reconstruction](https://mlsanity.com/paper/2412.08210/) (2024)
- [CANeRV: Content Adaptive Neural Representation for Video Compression](https://mlsanity.com/paper/2502.06181/) (2025)
- [Dataset Distillation as Data Compression: A Rate-Utility Perspective](https://mlsanity.com/paper/2507.17221/) (2025)
- [Compression as Adaptation: Implicit Visual Representation with Diffusion Foundation Models](https://mlsanity.com/paper/2603.07615/) (2026)
- [INR-MDSQC: Implicit Neural Representation Multiple Description Scalar Quantization for robust image Coding](https://mlsanity.com/paper/2306.13919/) (2023)
- [3D Compression Using Neural Fields](https://mlsanity.com/paper/2311.13009/) (2023)
- [Image Coding for Machines via Feature-Preserving Rate-Distortion Optimization](https://mlsanity.com/paper/2504.02216/) (2025)
- [TeCoNeRV: Leveraging Temporal Coherence for Compressible Neural Representations for Videos](https://mlsanity.com/paper/2602.16711/) (2026)
- [Implicit Neural Multiple Description for DNA-based data storage](https://mlsanity.com/paper/2309.06956/) (2023)

## Abstract (arXiv)

We introduce COOL-CHIC, a Coordinate-based Low Complexity Hierarchical Image Codec. It is a learned alternative to autoencoders with 629 parameters and 680 multiplications per decoded pixel. COOL-CHIC offers compression performance close to modern conventional MPEG codecs such as HEVC and is competitive with popular autoencoder-based systems. This method is inspired by Coordinate-based Neural Representations, where an image is represented as a learned function which maps pixel coordinates to RGB values. The parameters of the mapping function are then sent using entropy coding. At the receiver side, the compressed image is obtained by evaluating the mapping function for all pixel coordinates. COOL-CHIC implementation is made open-source.