# Learning-Based Conditional Image Coder Using Color Separation

arXiv: https://arxiv.org/abs/2212.06241 · HTML: https://mlsanity.com/paper/2212.06241/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2022.

## Built on by (most cited first, 6 of 6)

- [Efficient Contextformer: Spatio-Channel Window Attention for Fast Context Modeling in Learned Image Compression](https://mlsanity.com/paper/2306.14287/) (2023)
- [Color Learning for Image Compression](https://mlsanity.com/paper/2306.17460/) (2023)
- [SLIC: A Learned Image Codec Using Structure and Color](https://mlsanity.com/paper/2401.17246/) (2024)
- [Bit Distribution Study and Implementation of Spatial Quality Map in the JPEG-AI Standardization](https://mlsanity.com/paper/2402.17470/) (2024)
- [Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model](https://mlsanity.com/paper/2402.17487/) (2024)
- [A Study on the Effect of Color Spaces in Learned Image Compression](https://mlsanity.com/paper/2406.13709/) (2024)

## Abstract (arXiv)

Recently, image compression codecs based on Neural Networks(NN) outperformed the state-of-art classic ones such as BPG, an image format based on HEVC intra. However, the typical NN codec has high complexity, and it has limited options for parallel data processing. In this work, we propose a conditional separation principle that aims to improve parallelization and lower the computational requirements of an NN codec. We present a Conditional Color Separation (CCS) codec which follows this principle. The color components of an image are split into primary and non-primary ones. The processing of each component is done separately, by jointly trained networks. Our approach allows parallel processing of each component, flexibility to select different channel numbers, and an overall complexity reduction. The CCS codec uses over 40% less memory, has 2x faster encoding and 22% faster decoding speed, with only 4% BD-rate loss in RGB PSNR compared to our baseline model over BPG.