# Reducing The Amortization Gap of Entropy Bottleneck In End-to-End Image Compression

arXiv: https://arxiv.org/abs/2209.00964 · HTML: https://mlsanity.com/paper/2209.00964/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 2 Sep 2022.

## Builds on (most cited first, 17 of 17)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [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)
- [Lossy Image Compression with Compressive Autoencoders](https://mlsanity.com/paper/1703.00395/) (2017)
- [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)
- [Inference Suboptimality in Variational Autoencoders](https://mlsanity.com/paper/1801.03558/) (2018)
- [Enhanced Invertible Encoding for Learned Image Compression](https://mlsanity.com/paper/2108.03690/) (2021)
- [Improving Inference for Neural Image Compression](https://mlsanity.com/paper/2006.04240/) (2020)
- [Content Adaptive and Error Propagation Aware Deep Video Compression](https://mlsanity.com/paper/2003.11282/) (2020)
- [Content Adaptive Optimization for Neural Image Compression](https://mlsanity.com/paper/1906.01223/) (2019)
- [Overfitting for Fun and Profit: Instance-Adaptive Data Compression](https://mlsanity.com/paper/2101.08687/) (2021)
- [Efficient Adaptation of Neural Network Filter for Video Compression](https://mlsanity.com/paper/2007.14267/) (2020)
- [Block-optimized Variable Bit Rate Neural Image Compression](https://mlsanity.com/paper/1805.10887/) (2018)
- [Utilising Low Complexity CNNs to Lift Non-Local Redundancies in Video Coding](https://mlsanity.com/paper/1910.08737/) (2019)

## Built on by (most cited first, 7 of 7)

- [RQAT-INR: Improved Implicit Neural Image Compression](https://mlsanity.com/paper/2303.03028/) (2023)
- [Improved Positional Encoding for Implicit Neural Representation based Compact Data Representation](https://mlsanity.com/paper/2311.06059/) (2023)
- [Latent-Shift: Gradient of Entropy Helps Neural Codecs](https://mlsanity.com/paper/2308.00725/) (2023)
- [Reducing The Mismatch Between Marginal and Learned Distributions in Neural Video Compression](https://mlsanity.com/paper/2210.06596/) (2022)
- [Survey on Visual Signal Coding and Processing with Generative Models: Technologies, Standards and Optimization](https://mlsanity.com/paper/2405.14221/) (2024)
- [Learned Compression of Encoding Distributions](https://mlsanity.com/paper/2406.13059/) (2024)
- [Exploiting Latent Properties to Optimize Neural Codecs](https://mlsanity.com/paper/2501.01231/) (2025)

## Abstract (arXiv)

End-to-end deep trainable models are about to exceed the performance of the traditional handcrafted compression techniques on videos and images. The core idea is to learn a non-linear transformation, modeled as a deep neural network, mapping input image into latent space, jointly with an entropy model of the latent distribution. The decoder is also learned as a deep trainable network, and the reconstructed image measures the distortion. These methods enforce the latent to follow some prior distributions. Since these priors are learned by optimization over the entire training set, the performance is optimal in average. However, it cannot fit exactly on every single new instance, hence damaging the compression performance by enlarging the bit-stream. In this paper, we propose a simple yet efficient instance-based parameterization method to reduce this amortization gap at a minor cost. The proposed method is applicable to any end-to-end compressing methods, improving the compression bitrate by 1% without any impact on the reconstruction quality.