# Color Learning for Image Compression

arXiv: https://arxiv.org/abs/2306.17460 · HTML: https://mlsanity.com/paper/2306.17460/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 30 Jun 2023.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [CompressAI: a PyTorch library and evaluation platform for end-to-end compression research](https://mlsanity.com/paper/2011.03029/) (2020)
- [Density Modeling of Images using a Generalized Normalization Transformation](https://mlsanity.com/paper/1511.06281/) (2015)
- [Learning-Based Conditional Image Coder Using Color Separation](https://mlsanity.com/paper/2212.06241/) (2022)
- [Opening the Black Box of Learned Image Coders](https://mlsanity.com/paper/2202.13209/) (2022)

## Built on by (most cited first, 4 of 4)

- [Dataset Condensation with Color Compensation](https://mlsanity.com/paper/2508.01139/) (2025)
- [SLIC: A Learned Image Codec Using Structure and Color](https://mlsanity.com/paper/2401.17246/) (2024)
- [A Study on the Effect of Color Spaces in Learned Image Compression](https://mlsanity.com/paper/2406.13709/) (2024)
- [TreeNet: A Light Weight Model for Low Bitrate Image Compression](https://mlsanity.com/paper/2512.16743/) (2025)

## Abstract (arXiv)

Deep learning based image compression has gained a lot of momentum in recent times. To enable a method that is suitable for image compression and subsequently extended to video compression, we propose a novel deep learning model architecture, where the task of image compression is divided into two sub-tasks, learning structural information from luminance channel and color from chrominance channels. The model has two separate branches to process the luminance and chrominance components. The color difference metric CIEDE2000 is employed in the loss function to optimize the model for color fidelity. We demonstrate the benefits of our approach and compare the performance to other codecs. Additionally, the visualization and analysis of latent channel impulse response is performed.