# Learned Image Compression with Hierarchical Progressive Context Modeling

arXiv: https://arxiv.org/abs/2507.19125 · HTML: https://mlsanity.com/paper/2507.19125/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 54 references that are in the map.
- First posted on arXiv 25 Jul 2025.

## Builds on (most cited first, 30 of 37)

- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [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)
- [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [Understanding the Effective Receptive Field in Deep Convolutional Neural Networks](https://mlsanity.com/paper/1701.04128/) (2017)
- [ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding](https://mlsanity.com/paper/2203.10886/) (2022)
- [CompressAI: a PyTorch library and evaluation platform for end-to-end compression research](https://mlsanity.com/paper/2011.03029/) (2020)
- [Channel-wise Autoregressive Entropy Models for Learned Image Compression](https://mlsanity.com/paper/2007.08739/) (2020)
- [Deep Contextual Video Compression](https://mlsanity.com/paper/2109.15047/) (2021)
- [Learned Image Compression with Mixed Transformer-CNN Architectures](https://mlsanity.com/paper/2303.14978/) (2023)
- [Checkerboard Context Model for Efficient Learned Image Compression](https://mlsanity.com/paper/2103.15306/) (2021)
- [Neural Video Compression with Diverse Contexts](https://mlsanity.com/paper/2302.14402/) (2023)
- [FVC: A New Framework towards Deep Video Compression in Feature Space](https://mlsanity.com/paper/2105.09600/) (2021)
- [The Devil Is in the Details: Window-based Attention for Image Compression](https://mlsanity.com/paper/2203.08450/) (2022)
- [Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks](https://mlsanity.com/paper/2303.03667/) (2023)
- [Neural Video Compression with Feature Modulation](https://mlsanity.com/paper/2402.17414/) (2024)
- [Entroformer: A Transformer-based Entropy Model for Learned Image Compression](https://mlsanity.com/paper/2202.05492/) (2022)
- [Non-local Attention Optimized Deep Image Compression](https://mlsanity.com/paper/1904.09757/) (2019)
- [Towards Practical Real-Time Neural Video Compression](https://mlsanity.com/paper/2502.20762/) (2025)
- [A Unified End-to-End Framework for Efficient Deep Image Compression](https://mlsanity.com/paper/2002.03370/) (2020)
- [MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression](https://mlsanity.com/paper/2307.15421/) (2023)
- [Frequency-Aware Transformer for Learned Image Compression](https://mlsanity.com/paper/2310.16387/) (2023)
- [Soft then Hard: Rethinking the Quantization in Neural Image Compression](https://mlsanity.com/paper/2104.05168/) (2021)
- [USTC-TD: A Test Dataset and Benchmark for Image and Video Coding in 2020s](https://mlsanity.com/paper/2409.08481/) (2024)
- [MambaVC: Learned Visual Compression with Selective State Spaces](https://mlsanity.com/paper/2405.15413/) (2024)
- [Object Segmentation-Assisted Inter Prediction for Versatile Video Coding](https://mlsanity.com/paper/2403.11694/) (2024)
- [Neural Video Compression with Context Modulation](https://mlsanity.com/paper/2505.14541/) (2025)
- [M2T: Masking Transformers Twice for Faster Decoding](https://mlsanity.com/paper/2304.07313/) (2023)
- [Uniformly Accelerated Motion Model for Inter Prediction](https://mlsanity.com/paper/2407.11541/) (2024)

## Built on by (most cited first, 21 of 21)

- [Content-Aware Mamba for Learned Image Compression](https://mlsanity.com/paper/2508.02192/) (2025)
- [Differentiable Vector Quantization for Rate-Distortion Optimization of Generative Image Compression](https://mlsanity.com/paper/2604.10546/) (2026)
- [ProGIC: Progressive and Lightweight Generative Image Compression with Residual Vector Quantization](https://mlsanity.com/paper/2603.02897/) (2026)
- [Next-Frame Decoding for Ultra-Low-Bitrate Image Compression with Video Diffusion Priors](https://mlsanity.com/paper/2603.15129/) (2026)
- [Scaling Learned Image Compression Models up to 1 Billion](https://mlsanity.com/paper/2508.09075/) (2025)
- [Leveraging Second-Order Curvature for Efficient Learned Image Compression: Theory and Empirical Evidence](https://mlsanity.com/paper/2601.20769/) (2026)
- [Uni-LVC: A Unified Method for Intra- and Inter-Mode Learned Video Compression](https://mlsanity.com/paper/2603.05756/) (2026)
- [Inevitable Encounters: Backdoor Attacks Involving Lossy Compression](https://mlsanity.com/paper/2603.13864/) (2026)
- [Adaptive Learned Image Compression with Graph Neural Networks](https://mlsanity.com/paper/2603.25316/) (2026)
- [Learned Image Compression for Vision-Language-Action Models](https://mlsanity.com/paper/2606.16253/) (2026)
- [SDGIC: A Semantic Disambiguation-Guided Generative Image Compression Method for Ultra-Low Bitrates](https://mlsanity.com/paper/2512.06344/) (2025)
- [End-to-End Image Compression with Segmentation Guided Dual Coding for Wind Turbines](https://mlsanity.com/paper/2603.29927/) (2026)
- [Control Your View: High-Resolution Global Semantic Manipulation in Learned Image Compression](https://mlsanity.com/paper/2605.08727/) (2026)
- [Efficient Learned Image Compression without Entropy Coding](https://mlsanity.com/paper/2605.23323/) (2026)
- [ChWDTA: Channel-wise Wavelet-Domain Transformer Attention and Entropy Modeling for Learned Image Compression](https://mlsanity.com/paper/2606.00111/) (2026)
- [Mixture-of-Experts-based Entropy Model for Learned Image Compression](https://mlsanity.com/paper/2608.10947/) (2026)
- [Multiple Scale Latents for Learned Image Compression](https://mlsanity.com/paper/2608.10952/) (2026)
- [Every Packet Counts: Dispersing Information for Loss-Resilient Learned Image Compression](https://mlsanity.com/paper/2608.11096/) (2026)
- [Fast Test-Time Refinement for Robust Learned Image Compression](https://mlsanity.com/paper/2608.15113/) (2026)
- [Distributed Semantic Segmentation With Improved Rate-Distortion Trade-Off](https://mlsanity.com/paper/2608.28684/) (2026)
- [Recasting DiT for Extreme Image Compression with Hierarchical Visual Priors](https://mlsanity.com/paper/2610.18607/) (2026)

## Abstract (arXiv)

Context modeling is essential in learned image compression for accurately estimating the distribution of latents. While recent advanced methods have expanded context modeling capacity, they still struggle to efficiently exploit long-range dependency and diverse context information across different coding steps. In this paper, we introduce a novel Hierarchical Progressive Context Model (HPCM) for more efficient context information acquisition. Specifically, HPCM employs a hierarchical coding schedule to sequentially model the contextual dependencies among latents at multiple scales, which enables more efficient long-range context modeling. Furthermore, we propose a progressive context fusion mechanism that incorporates contextual information from previous coding steps into the current step, effectively exploiting diverse contextual information. Experimental results demonstrate that our method achieves state-of-the-art rate-distortion performance and strikes a better balance between compression performance and computational complexity. The code is available at https://github.com/lyq133/LIC-HPCM.