# A Unified End-to-End Framework for Efficient Deep Image Compression

arXiv: https://arxiv.org/abs/2002.03370 · HTML: https://mlsanity.com/paper/2002.03370/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2020.

## Built on by (most cited first, 30 of 58)

- [ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding](https://mlsanity.com/paper/2203.10886/) (2022)
- [Enhanced Invertible Encoding for Learned Image Compression](https://mlsanity.com/paper/2108.03690/) (2021)
- [MLIC: Multi-Reference Entropy Model for Learned Image Compression](https://mlsanity.com/paper/2211.07273/) (2022)
- [ELF-VC: Efficient Learned Flexible-Rate Video Coding](https://mlsanity.com/paper/2104.14335/) (2021)
- [Transformer-based Image Compression](https://mlsanity.com/paper/2111.06707/) (2021)
- [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)
- [USTC-TD: A Test Dataset and Benchmark for Image and Video Coding in 2020s](https://mlsanity.com/paper/2409.08481/) (2024)
- [Learned Image Compression with Hierarchical Progressive Context Modeling](https://mlsanity.com/paper/2507.19125/) (2025)
- [High-Efficiency Lossy Image Coding Through Adaptive Neighborhood Information Aggregation](https://mlsanity.com/paper/2204.11448/) (2022)
- [Ultra Lowrate Image Compression with Semantic Residual Coding and Compression-aware Diffusion](https://mlsanity.com/paper/2505.08281/) (2025)
- [Efficient Visual Computing with Camera RAW Snapshots](https://mlsanity.com/paper/2212.07778/) (2022)
- [Content Adaptive Latents and Decoder for Neural Image Compression](https://mlsanity.com/paper/2212.10132/) (2022)
- [All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation](https://mlsanity.com/paper/2409.19660/) (2024)
- [IICNet: A Generic Framework for Reversible Image Conversion](https://mlsanity.com/paper/2109.04242/) (2021)
- [Conditional Latent Coding with Learnable Synthesized Reference for Deep Image Compression](https://mlsanity.com/paper/2502.09971/) (2025)
- [Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression](https://mlsanity.com/paper/2211.02854/) (2022)
- [High-Fidelity Variable-Rate Image Compression via Invertible Activation Transformation](https://mlsanity.com/paper/2209.05054/) (2022)
- [Region-Adaptive Transform with Segmentation Prior for Image Compression](https://mlsanity.com/paper/2403.00628/) (2024)
- [Generalized Gaussian Model for Learned Image Compression](https://mlsanity.com/paper/2411.19320/) (2024)
- [Transformer-based Variable-rate Image Compression with Region-of-interest Control](https://mlsanity.com/paper/2305.10807/) (2023)
- [Optimizing Image Compression via Joint Learning with Denoising](https://mlsanity.com/paper/2207.10869/) (2022)
- [Another Way to the Top: Exploit Contextual Clustering in Learned Image Coding](https://mlsanity.com/paper/2401.11615/) (2024)
- [Controllable Distortion-Perception Tradeoff Through Latent Diffusion for Neural Image Compression](https://mlsanity.com/paper/2412.11379/) (2024)
- [Content-Aware Mamba for Learned Image Compression](https://mlsanity.com/paper/2508.02192/) (2025)
- [Dual-Representation Image Compression at Ultra-Low Bitrates via Explicit Semantics and Implicit Textures](https://mlsanity.com/paper/2602.05213/) (2026)
- [End-to-end optimized image compression with competition of prior distributions](https://mlsanity.com/paper/2111.09172/) (2021)
- [RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression](https://mlsanity.com/paper/2203.10183/) (2022)
- [Super-High-Fidelity Image Compression via Hierarchical-ROI and Adaptive Quantization](https://mlsanity.com/paper/2403.13030/) (2024)
- [Multi-Scale Invertible Neural Network for Wide-Range Variable-Rate Learned Image Compression](https://mlsanity.com/paper/2503.21284/) (2025)

## Abstract (arXiv)

Image compression is a widely used technique to reduce the spatial redundancy in images. Recently, learning based image compression has achieved significant progress by using the powerful representation ability from neural networks. However, the current state-of-the-art learning based image compression methods suffer from the huge computational cost, which limits their capacity for practical applications. In this paper, we propose a unified framework called Efficient Deep Image Compression (EDIC) based on three new technologies, including a channel attention module, a Gaussian mixture model and a decoder-side enhancement module. Specifically, we design an auto-encoder style network for learning based image compression. To improve the coding efficiency, we exploit the channel relationship between latent representations by using the channel attention module. Besides, the Gaussian mixture model is introduced for the entropy model and improves the accuracy for bitrate estimation. Furthermore, we introduce the decoder-side enhancement module to further improve image compression performance. Our EDIC method can also be readily incorporated with the Deep Video Compression (DVC) framework to further improve the video compression performance. Simultaneously, our EDIC method boosts the coding performance significantly while bringing slightly increased computational cost. More importantly, experimental results demonstrate that the proposed approach outperforms the current state-of-the-art image compression methods and is up to more than 150 times faster in terms of decoding speed when compared with Minnen's method. The proposed framework also successfully improves the performance of the recent deep video compression system DVC. Our code will be released at https://github.com/liujiaheng/compression.