# Causal Context Adjustment Loss for Learned Image Compression

arXiv: https://arxiv.org/abs/2410.04847 · HTML: https://mlsanity.com/paper/2410.04847/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 49 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Simple Baselines for Image Restoration](https://mlsanity.com/paper/2204.04676/) (2022)
- [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)
- [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)
- [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)
- [The Devil Is in the Details: Window-based Attention for Image Compression](https://mlsanity.com/paper/2203.08450/) (2022)
- [Density Modeling of Images using a Generalized Normalization Transformation](https://mlsanity.com/paper/1511.06281/) (2015)
- [Entroformer: A Transformer-based Entropy Model for Learned Image Compression](https://mlsanity.com/paper/2202.05492/) (2022)
- [Enhanced Invertible Encoding for Learned Image Compression](https://mlsanity.com/paper/2108.03690/) (2021)
- [Non-local Attention Optimized Deep Image Compression](https://mlsanity.com/paper/1904.09757/) (2019)
- [Improving Inference for Neural Image Compression](https://mlsanity.com/paper/2006.04240/) (2020)
- [Causal Contextual Prediction for Learned Image Compression](https://mlsanity.com/paper/2011.09704/) (2020)
- [Transformer-based Image Compression](https://mlsanity.com/paper/2111.06707/) (2021)
- [Frequency-Aware Transformer for Learned Image Compression](https://mlsanity.com/paper/2310.16387/) (2023)
- [Joint Global and Local Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/2112.04487/) (2021)
- [Learning Accurate Entropy Model with Global Reference for Image Compression](https://mlsanity.com/paper/2010.08321/) (2020)
- [Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression](https://mlsanity.com/paper/2203.02452/) (2022)
- [Deep Convolutional AutoEncoder-based Lossy Image Compression](https://mlsanity.com/paper/1804.09535/) (2018)
- [Slimmable Compressive Autoencoders for Practical Neural Image Compression](https://mlsanity.com/paper/2103.15726/) (2021)
- [Efficient and Effective Context-Based Convolutional Entropy Modeling for Image Compression](https://mlsanity.com/paper/1906.10057/) (2019)
- [QARV: Quantization-Aware ResNet VAE for Lossy Image Compression](https://mlsanity.com/paper/2302.08899/) (2023)
- [Neural Data-Dependent Transform for Learned Image Compression](https://mlsanity.com/paper/2203.04963/) (2022)

## Built on by (most cited first, 19 of 19)

- [Learned Image Compression with Dictionary-based Entropy Model](https://mlsanity.com/paper/2504.00496/) (2025)
- [OSCAR: One-Step Diffusion Codec Across Multiple Bit-rates](https://mlsanity.com/paper/2505.16091/) (2025)
- [Content-Aware Mamba for Learned Image Compression](https://mlsanity.com/paper/2508.02192/) (2025)
- [Balanced Rate-Distortion Optimization in Learned Image Compression](https://mlsanity.com/paper/2502.20161/) (2025)
- [Dual-Representation Image Compression at Ultra-Low Bitrates via Explicit Semantics and Implicit Textures](https://mlsanity.com/paper/2602.05213/) (2026)
- [Differentiable Vector Quantization for Rate-Distortion Optimization of Generative Image Compression](https://mlsanity.com/paper/2604.10546/) (2026)
- [Neural Stereo Video Compression with Hybrid Disparity Compensation](https://mlsanity.com/paper/2504.20383/) (2025)
- [Leveraging Second-Order Curvature for Efficient Learned Image Compression: Theory and Empirical Evidence](https://mlsanity.com/paper/2601.20769/) (2026)
- [FIPER: Factorized Features for Robust Image Super-Resolution and Compression](https://mlsanity.com/paper/2410.18083/) (2024)
- [Generative Image Compression by Estimating Gradients of the Rate-variable Feature Distribution](https://mlsanity.com/paper/2505.20984/) (2025)
- [Deeply-Conditioned Image Compression via Self-Generated Priors](https://mlsanity.com/paper/2510.24437/) (2025)
- [Adaptive Learned Image Compression with Graph Neural Networks](https://mlsanity.com/paper/2603.25316/) (2026)
- [S2CFormer: Revisiting the RD-Latency Trade-off in Transformer-based Learned Image Compression](https://mlsanity.com/paper/2502.00700/) (2025)
- [HiDE: Hierarchical Dictionary-Based Entropy Modeling for Learned Image Compression](https://mlsanity.com/paper/2603.06766/) (2026)
- [ARCHE: Autoregressive Residual Compression with Hyperprior and Excitation](https://mlsanity.com/paper/2603.10188/) (2026)
- [End-to-End Image Compression with Segmentation Guided Dual Coding for Wind Turbines](https://mlsanity.com/paper/2603.29927/) (2026)
- [What and Where to Adapt: Structure-Semantics Co-Tuning for Machine Vision Compression via Synergistic Adapters](https://mlsanity.com/paper/2604.10017/) (2026)
- [Control Your View: High-Resolution Global Semantic Manipulation in Learned Image Compression](https://mlsanity.com/paper/2605.08727/) (2026)
- [Fast Test-Time Refinement for Robust Learned Image Compression](https://mlsanity.com/paper/2608.15113/) (2026)

## Abstract (arXiv)

In recent years, learned image compression (LIC) technologies have surpassed conventional methods notably in terms of rate-distortion (RD) performance. Most present learned techniques are VAE-based with an autoregressive entropy model, which obviously promotes the RD performance by utilizing the decoded causal context. However, extant methods are highly dependent on the fixed hand-crafted causal context. The question of how to guide the auto-encoder to generate a more effective causal context benefit for the autoregressive entropy models is worth exploring. In this paper, we make the first attempt in investigating the way to explicitly adjust the causal context with our proposed Causal Context Adjustment loss (CCA-loss). By imposing the CCA-loss, we enable the neural network to spontaneously adjust important information into the early stage of the autoregressive entropy model. Furthermore, as transformer technology develops remarkably, variants of which have been adopted by many state-of-the-art (SOTA) LIC techniques. The existing computing devices have not adapted the calculation of the attention mechanism well, which leads to a burden on computation quantity and inference latency. To overcome it, we establish a convolutional neural network (CNN) image compression model and adopt the unevenly channel-wise grouped strategy for high efficiency. Ultimately, the proposed CNN-based LIC network trained with our Causal Context Adjustment loss attains a great trade-off between inference latency and rate-distortion performance.