# SLIC: A Learned Image Codec Using Structure and Color

arXiv: https://arxiv.org/abs/2401.17246 · HTML: https://mlsanity.com/paper/2401.17246/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 10 of 10)

- [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)
- [ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding](https://mlsanity.com/paper/2203.10886/) (2022)
- [Transformer-based Image Compression](https://mlsanity.com/paper/2111.06707/) (2021)
- [SA-Net: Shuffle Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/2102.00240/) (2021)
- [RQAT-INR: Improved Implicit Neural Image Compression](https://mlsanity.com/paper/2303.03028/) (2023)
- [Learning-Based Conditional Image Coder Using Color Separation](https://mlsanity.com/paper/2212.06241/) (2022)
- [Color Learning for Image Compression](https://mlsanity.com/paper/2306.17460/) (2023)
- [Processing Energy Modeling for Neural Network Based Image Compression](https://mlsanity.com/paper/2306.16755/) (2023)

## Built on by (most cited first, 2 of 2)

- [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)

We propose the structure and color based learned image codec (SLIC) in which the task of compression is split into that of luminance and chrominance. The deep learning model is built with a novel multi-scale architecture for Y and UV channels in the encoder, where the features from various stages are combined to obtain the latent representation. An autoregressive context model is employed for backward adaptation and a hyperprior block for forward adaptation. Various experiments are carried out to study and analyze the performance of the proposed model, and to compare it with other image codecs. We also illustrate the advantages of our method through the visualization of channel impulse responses, latent channels and various ablation studies. The model achieves Bjontegaard delta bitrate gains of 7.5% and 4.66% in terms of MS-SSIM and CIEDE2000 metrics with respect to other state-of-the-art reference codecs.