# High-Efficiency Lossy Image Coding Through Adaptive Neighborhood Information Aggregation

arXiv: https://arxiv.org/abs/2204.11448 · HTML: https://mlsanity.com/paper/2204.11448/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 75 references that are in the map.
- First posted on arXiv 25 Apr 2022.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [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)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [Early Convolutions Help Transformers See Better](https://mlsanity.com/paper/2106.14881/) (2021)
- [Dynamic Filter Networks](https://mlsanity.com/paper/1605.09673/) (2016)
- [ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding](https://mlsanity.com/paper/2203.10886/) (2022)
- [DeepViT: Towards Deeper Vision Transformer](https://mlsanity.com/paper/2103.11886/) (2021)
- [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)
- [Neighborhood Attention Transformer](https://mlsanity.com/paper/2204.07143/) (2022)
- [Checkerboard Context Model for Efficient Learned Image Compression](https://mlsanity.com/paper/2103.15306/) (2021)
- [How Much Position Information Do Convolutional Neural Networks Encode?](https://mlsanity.com/paper/2001.08248/) (2020)
- [Nonlinear Transform Coding](https://mlsanity.com/paper/2007.03034/) (2020)
- [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)
- [Transformer-based Image Compression](https://mlsanity.com/paper/2111.06707/) (2021)

## Built on by (most cited first, 20 of 20)

- [QARV: Quantization-Aware ResNet VAE for Lossy Image Compression](https://mlsanity.com/paper/2302.08899/) (2023)
- [Efficient Visual Computing with Camera RAW Snapshots](https://mlsanity.com/paper/2212.07778/) (2022)
- [All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation](https://mlsanity.com/paper/2409.19660/) (2024)
- [Fast and High-Performance Learned Image Compression With Improved Checkerboard Context Model, Deformable Residual Module, and Knowledge Distillation](https://mlsanity.com/paper/2309.02529/) (2023)
- [Unifying Generation and Compression: Ultra-low bitrate Image Coding Via Multi-stage Transformer](https://mlsanity.com/paper/2403.03736/) (2024)
- [Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression](https://mlsanity.com/paper/2211.02854/) (2022)
- [Bridging Compressed Image Latents and Multimodal Large Language Models](https://mlsanity.com/paper/2407.19651/) (2024)
- [Another Way to the Top: Exploit Contextual Clustering in Learned Image Coding](https://mlsanity.com/paper/2401.11615/) (2024)
- [Multistage Spatial Context Models for Learned Image Compression](https://mlsanity.com/paper/2302.09263/) (2023)
- [Multi-Scale Invertible Neural Network for Wide-Range Variable-Rate Learned Image Compression](https://mlsanity.com/paper/2503.21284/) (2025)
- [Exploring Long- and Short-Range Temporal Information for Learned Video Compression](https://mlsanity.com/paper/2208.03754/) (2022)
- [Theoretical Bound-Guided Hierarchical VAE for Neural Image Codecs](https://mlsanity.com/paper/2403.18535/) (2024)
- [Exploring Effective Mask Sampling Modeling for Neural Image Compression](https://mlsanity.com/paper/2306.05704/) (2023)
- [High-Efficiency Neural Video Compression via Hierarchical Predictive Learning](https://mlsanity.com/paper/2410.02598/) (2024)
- [FD-LSCIC: Frequency Decomposition-based Learned Screen Content Image Compression](https://mlsanity.com/paper/2502.15174/) (2025)
- [Perception-Oriented Latent Coding for High-Performance Compressed Domain Semantic Inference](https://mlsanity.com/paper/2507.01608/) (2025)
- [Dynamic Kernel-Based Adaptive Spatial Aggregation for Learned Image Compression](https://mlsanity.com/paper/2308.08723/) (2023)
- [Interleaved Block-based Learned Image Compression with Feature Enhancement and Quantization Error Compensation](https://mlsanity.com/paper/2502.15188/) (2025)
- [End-to-End Semantic Preservation in Text-Aware Image Compression Systems](https://mlsanity.com/paper/2503.19495/) (2025)
- [Visual Token Codec: Unleashing Spatial Redundancy for ViT Feature Coding](https://mlsanity.com/paper/2608.08832/) (2026)

## Abstract (arXiv)

Questing for learned lossy image coding (LIC) with superior compression performance and computation throughput is challenging. The vital factor behind it is how to intelligently explore Adaptive Neighborhood Information Aggregation (ANIA) in transform and entropy coding modules. To this end, Integrated Convolution and Self-Attention (ICSA) unit is first proposed to form a content-adaptive transform to characterize and embed neighborhood information dynamically of any input. Then a Multistage Context Model (MCM) is devised to progressively use available neighbors following a pre-arranged spatial-channel order for accurate probability estimation in parallel. ICSA and MCM are stacked under a Variational AutoEncoder (VAE) architecture to derive rate-distortion optimized compact representation of input image via end-to-end learning. Our method reports state-of-the-art compression performance surpassing the VVC Intra and other prevalent LIC approaches across Kodak, CLIC, and Tecnick datasets; More importantly, our method offers >60× decoding speedup using a comparable-size model when compared with the most popular LIC method. All materials are made publicly accessible at https://njuvision.github.io/TinyLIC for reproducible research.