# Learning Accurate Entropy Model with Global Reference for Image Compression

arXiv: https://arxiv.org/abs/2010.08321 · HTML: https://mlsanity.com/paper/2010.08321/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 30 of 45)

- [Neural Video Compression with Diverse Contexts](https://mlsanity.com/paper/2302.14402/) (2023)
- [CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2109.06165/) (2021)
- [Entroformer: A Transformer-based Entropy Model for Learned Image Compression](https://mlsanity.com/paper/2202.05492/) (2022)
- [MLIC: Multi-Reference Entropy Model for Learned Image Compression](https://mlsanity.com/paper/2211.07273/) (2022)
- [MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression](https://mlsanity.com/paper/2307.15421/) (2023)
- [Joint Global and Local Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/2112.04487/) (2021)
- [Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression](https://mlsanity.com/paper/2203.02452/) (2022)
- [Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression](https://mlsanity.com/paper/2203.10897/) (2022)
- [Lossy Image Compression with Quantized Hierarchical VAEs](https://mlsanity.com/paper/2208.13056/) (2022)
- [QARV: Quantization-Aware ResNet VAE for Lossy Image Compression](https://mlsanity.com/paper/2302.08899/) (2023)
- [HEMGS: A Hybrid Entropy Model for 3D Gaussian Splatting Data Compression](https://mlsanity.com/paper/2411.18473/) (2024)
- [Learned Image Compression with Dictionary-based Entropy Model](https://mlsanity.com/paper/2504.00496/) (2025)
- [Towards End-to-End Image Compression and Analysis with Transformers](https://mlsanity.com/paper/2112.09300/) (2021)
- [Deep Lossy Plus Residual Coding for Lossless and Near-lossless Image Compression](https://mlsanity.com/paper/2209.04847/) (2022)
- [High-Efficiency Lossy Image Coding Through Adaptive Neighborhood Information Aggregation](https://mlsanity.com/paper/2204.11448/) (2022)
- [Causal Context Adjustment Loss for Learned Image Compression](https://mlsanity.com/paper/2410.04847/) (2024)
- [Frequency Disentangled Features in Neural Image Compression](https://mlsanity.com/paper/2308.02620/) (2023)
- [Efficient Contextformer: Spatio-Channel Window Attention for Fast Context Modeling in Learned Image Compression](https://mlsanity.com/paper/2306.14287/) (2023)
- [QVRF: A Quantization-error-aware Variable Rate Framework for Learned Image Compression](https://mlsanity.com/paper/2303.05744/) (2023)
- [Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression](https://mlsanity.com/paper/2211.02854/) (2022)
- [ECSIC: Epipolar Cross Attention for Stereo Image Compression](https://mlsanity.com/paper/2307.10284/) (2023)
- [LLIC: Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression](https://mlsanity.com/paper/2304.09571/) (2023)
- [Multi-Context Dual Hyper-Prior Neural Image Compression](https://mlsanity.com/paper/2309.10799/) (2023)
- [Another Way to the Top: Exploit Contextual Clustering in Learned Image Coding](https://mlsanity.com/paper/2401.11615/) (2024)
- [GroupedMixer: An Entropy Model with Group-wise Token-Mixers for Learned Image Compression](https://mlsanity.com/paper/2405.01170/) (2024)
- [An Efficient Adaptive Compression Method for Human Perception and Machine Vision Tasks](https://mlsanity.com/paper/2501.04329/) (2025)
- [Spatiotemporal Entropy Model is All You Need for Learned Video Compression](https://mlsanity.com/paper/2104.06083/) (2021)
- [Laplacian-guided Entropy Model in Neural Codec with Blur-dissipated Synthesis](https://mlsanity.com/paper/2403.16258/) (2024)
- [Diversify, Contextualize, and Adapt: Efficient Entropy Modeling for Neural Image Codec](https://mlsanity.com/paper/2411.05832/) (2024)
- [Learned Focused Plenoptic Image Compression with Microimage Preprocessing and Global Attention](https://mlsanity.com/paper/2305.00489/) (2023)

## Abstract (arXiv)

In recent deep image compression neural networks, the entropy model plays a critical role in estimating the prior distribution of deep image encodings. Existing methods combine hyperprior with local context in the entropy estimation function. This greatly limits their performance due to the absence of a global vision. In this work, we propose a novel Global Reference Model for image compression to effectively leverage both the local and the global context information, leading to an enhanced compression rate. The proposed method scans decoded latents and then finds the most relevant latent to assist the distribution estimating of the current latent. A by-product of this work is the innovation of a mean-shifting GDN module that further improves the performance. Experimental results demonstrate that the proposed model outperforms the rate-distortion performance of most of the state-of-the-art methods in the industry.