# Image Compression with Recurrent Neural Network and Generalized Divisive Normalization

arXiv: https://arxiv.org/abs/2109.01999 · HTML: https://mlsanity.com/paper/2109.01999/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 12 references that are in the map.
- First posted on arXiv 5 Sep 2021.

## Builds on (most cited first, 11 of 11)

- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018)
- [Full Resolution Image Compression with Recurrent Neural Networks](https://mlsanity.com/paper/1608.05148/) (2016)
- [Variable Rate Image Compression with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06085/) (2015)
- [Context-adaptive Entropy Model for End-to-end Optimized Image Compression](https://mlsanity.com/paper/1809.10452/) (2018)
- [Density Modeling of Images using a Generalized Normalization Transformation](https://mlsanity.com/paper/1511.06281/) (2015)
- [Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks](https://mlsanity.com/paper/1703.10114/) (2017)
- [End-to-end optimization of nonlinear transform codes for perceptual quality](https://mlsanity.com/paper/1607.05006/) (2016)
- [Target-Quality Image Compression with Recurrent, Convolutional Neural Networks](https://mlsanity.com/paper/1705.06687/) (2017)
- [Observer Dependent Lossy Image Compression](https://mlsanity.com/paper/1910.03472/) (2019)

## Built on by (most cited first, 9 of 9)

- [DPICT: Deep Progressive Image Compression Using Trit-Planes](https://mlsanity.com/paper/2112.06334/) (2021)
- [Learning-Driven Lossy Image Compression; A Comprehensive Survey](https://mlsanity.com/paper/2201.09240/) (2022)
- [Onboard deep lossless and near-lossless predictive coding of hyperspectral images with line-based attention](https://mlsanity.com/paper/2403.17677/) (2024)
- [MALICE: Manipulation Attacks on Learned Image ComprEssion](https://mlsanity.com/paper/2205.13253/) (2022)
- [3D Point Cloud Compression with Recurrent Neural Network and Image Compression Methods](https://mlsanity.com/paper/2402.11680/) (2024)
- [Rethinking Learned Image Compression: Context is All You Need](https://mlsanity.com/paper/2407.11590/) (2024)
- [Learned Compression of Point Cloud Geometry and Attributes in a Single Model through Multimodal Rate-Control](https://mlsanity.com/paper/2408.00599/) (2024)
- [ProDAT: Progressive Density-Aware Tail-Drop for Point Cloud Coding](https://mlsanity.com/paper/2510.17068/) (2025)
- [Deep Joint Source-Channel Coding for Wireless Video Transmission with Asymmetric Context](https://mlsanity.com/paper/2601.06170/) (2026)

## Abstract (arXiv)

Image compression is a method to remove spatial redundancy between adjacent pixels and reconstruct a high-quality image. In the past few years, deep learning has gained huge attention from the research community and produced promising image reconstruction results. Therefore, recent methods focused on developing deeper and more complex networks, which significantly increased network complexity. In this paper, two effective novel blocks are developed: analysis and synthesis block that employs the convolution layer and Generalized Divisive Normalization (GDN) in the variable-rate encoder and decoder side. Our network utilizes a pixel RNN approach for quantization. Furthermore, to improve the whole network, we encode a residual image using LSTM cells to reduce unnecessary information. Experimental results demonstrated that the proposed variable-rate framework with novel blocks outperforms existing methods and standard image codecs, such as George's ~cite002 and JPEG in terms of image similarity. The project page along with code and models are available at https://khawar512.github.io/cvpr/