# Image-Dependent Local Entropy Models for Learned Image Compression

arXiv: https://arxiv.org/abs/1805.12295 · HTML: https://mlsanity.com/paper/1805.12295/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2018.

## Built on by (most cited first, 12 of 12)

- [Checkerboard Context Model for Efficient Learned Image Compression](https://mlsanity.com/paper/2103.15306/) (2021)
- [Neural Data-Dependent Transform for Learned Image Compression](https://mlsanity.com/paper/2203.04963/) (2022)
- [Learned Image Compression with Dictionary-based Entropy Model](https://mlsanity.com/paper/2504.00496/) (2025)
- [End-to-end optimized image compression with competition of prior distributions](https://mlsanity.com/paper/2111.09172/) (2021)
- [Delta-ICM: Entropy Modeling with Delta Function for Learned Image Compression](https://mlsanity.com/paper/2410.07669/) (2024)
- [Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions](https://mlsanity.com/paper/2108.13055/) (2021)
- [ConvNeXt-ChARM: ConvNeXt-based Transform for Efficient Neural Image Compression](https://mlsanity.com/paper/2307.06342/) (2023)
- [An Efficient Implicit Neural Representation Image Codec Based on Mixed Autoregressive Model for Low-Complexity Decoding](https://mlsanity.com/paper/2401.12587/) (2024)
- [AICT: An Adaptive Image Compression Transformer](https://mlsanity.com/paper/2307.06091/) (2023)
- [Texture for Colors: Natural Representations of Colors Using Variable Bit-Depth Textures](https://mlsanity.com/paper/2105.01768/) (2021)
- [Entropy-driven Unsupervised Keypoint Representation Learning in Videos](https://mlsanity.com/paper/2209.15404/) (2022)
- [HiDE: Hierarchical Dictionary-Based Entropy Modeling for Learned Image Compression](https://mlsanity.com/paper/2603.06766/) (2026)

## Abstract (arXiv)

The leading approach for image compression with artificial neural networks (ANNs) is to learn a nonlinear transform and a fixed entropy model that are optimized for rate-distortion performance. We show that this approach can be significantly improved by incorporating spatially local, image-dependent entropy models. The key insight is that existing ANN-based methods learn an entropy model that is shared between the encoder and decoder, but they do not transmit any side information that would allow the model to adapt to the structure of a specific image. We present a method for augmenting ANN-based image coders with image-dependent side information that leads to a 17.8% rate reduction over a state-of-the-art ANN-based baseline model on a standard evaluation set, and 70-98% reductions on images with low visual complexity that are poorly captured by a fixed, global entropy model.