# Estimating the Resize Parameter in End-to-end Learned Image Compression

arXiv: https://arxiv.org/abs/2204.12022 · HTML: https://mlsanity.com/paper/2204.12022/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 75 references that are in the map.
- First posted on arXiv 26 Apr 2022.

## Builds on (most cited first, 30 of 31)

- [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)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [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)
- [High-Fidelity Generative Image Compression](https://mlsanity.com/paper/2006.09965/) (2020)
- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [MAXIM: Multi-Axis MLP for Image Processing](https://mlsanity.com/paper/2201.02973/) (2022)
- [Lossy Image Compression with Compressive Autoencoders](https://mlsanity.com/paper/1703.00395/) (2017)
- [Generative Adversarial Networks for Extreme Learned Image Compression](https://mlsanity.com/paper/1804.02958/) (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)
- [Video Compression through Image Interpolation](https://mlsanity.com/paper/1804.06919/) (2018)
- [Context-adaptive Entropy Model for End-to-end Optimized Image Compression](https://mlsanity.com/paper/1809.10452/) (2018)
- [Conditional Probability Models for Deep Image Compression](https://mlsanity.com/paper/1801.04260/) (2018)
- [Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks](https://mlsanity.com/paper/1703.10114/) (2017)
- [Non-local Attention Optimized Deep Image Compression](https://mlsanity.com/paper/1904.09757/) (2019)
- [Learned Video Compression](https://mlsanity.com/paper/1811.06981/) (2018)
- [A Technical Overview of AV1](https://mlsanity.com/paper/2008.06091/) (2020)
- [Neural Video Coding using Multiscale Motion Compensation and Spatiotemporal Context Model](https://mlsanity.com/paper/2007.04574/) (2020)
- [Learning Image and Video Compression through Spatial-Temporal Energy Compaction](https://mlsanity.com/paper/1906.09683/) (2019)
- [Neural Multi-scale Image Compression](https://mlsanity.com/paper/1805.06386/) (2018)
- [ViSTRA2: Video Coding using Spatial Resolution and Effective Bit Depth Adaptation](https://mlsanity.com/paper/1911.02833/) (2019)
- [Speeding up VP9 Intra Encoder with Hierarchical Deep Learning Based Partition Prediction](https://mlsanity.com/paper/1906.06476/) (2019)
- [GAN- vs. JPEG2000 Image Compression for Distributed Automotive Perception: Higher Peak SNR Does Not Mean Better Semantic Segmentation](https://mlsanity.com/paper/1902.04311/) (2019)
- [Self-Supervised Learning of Perceptually Optimized Block Motion Estimates for Video Compression](https://mlsanity.com/paper/2110.01805/) (2021)

## Built on by (most cited first, 5 of 5)

- [MULLER: Multilayer Laplacian Resizer for Vision](https://mlsanity.com/paper/2304.02859/) (2023)
- [AnimateZoo: Zero-shot Video Generation of Cross-Species Animation via Subject Alignment](https://mlsanity.com/paper/2404.04946/) (2024)
- [AICT: An Adaptive Image Compression Transformer](https://mlsanity.com/paper/2307.06091/) (2023)
- [Joint Hierarchical Priors and Adaptive Spatial Resolution for Efficient Neural Image Compression](https://mlsanity.com/paper/2307.02273/) (2023)
- [LiVeAction: a Lightweight, Versatile, and Asymmetric Neural Codec Design for Real-time Operation](https://mlsanity.com/paper/2605.06628/) (2026)

## Abstract (arXiv)

We describe a search-free resizing framework that can further improve the rate-distortion tradeoff of recent learned image compression models. Our approach is simple: compose a pair of differentiable downsampling/upsampling layers that sandwich a neural compression model. To determine resize factors for different inputs, we utilize another neural network jointly trained with the compression model, with the end goal of minimizing the rate-distortion objective. Our results suggest that "compression friendly" downsampled representations can be quickly determined during encoding by using an auxiliary network and differentiable image warping. By conducting extensive experimental tests on existing deep image compression models, we show results that our new resizing parameter estimation framework can provide Bjontegaard-Delta rate (BD-rate) improvement of about 10% against leading perceptual quality engines. We also carried out a subjective quality study, the results of which show that our new approach yields favorable compressed images. To facilitate reproducible research in this direction, the implementation used in this paper is being made freely available online at: https://github.com/treammm/ResizeCompression.