# Post-Training Quantization for Cross-Platform Learned Image Compression

arXiv: https://arxiv.org/abs/2202.07513 · HTML: https://mlsanity.com/paper/2202.07513/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 18 of 18)

- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [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 Step Size Quantization](https://mlsanity.com/paper/1902.08153/) (2019)
- [Up or Down? Adaptive Rounding for Post-Training Quantization](https://mlsanity.com/paper/2004.10568/) (2020)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [Quantizing deep convolutional networks for efficient inference: A whitepaper](https://mlsanity.com/paper/1806.08342/) (2018)
- [BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction](https://mlsanity.com/paper/2102.05426/) (2021)
- [A White Paper on Neural Network Quantization](https://mlsanity.com/paper/2106.08295/) (2021)
- [Data-Free Quantization Through Weight Equalization and Bias Correction](https://mlsanity.com/paper/1906.04721/) (2019)
- [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)
- [Checkerboard Context Model for Efficient Learned Image Compression](https://mlsanity.com/paper/2103.15306/) (2021)
- [LSQ+: Improving low-bit quantization through learnable offsets and better initialization](https://mlsanity.com/paper/2004.09576/) (2020)
- [Causal Contextual Prediction for Learned Image Compression](https://mlsanity.com/paper/2011.09704/) (2020)
- [Efficient Nonlinear Transforms for Lossy Image Compression](https://mlsanity.com/paper/1802.00847/) (2018)
- [Efficient Integer-Arithmetic-Only Convolutional Neural Networks](https://mlsanity.com/paper/2006.11735/) (2020)

## Built on by (most cited first, 14 of 14)

- [Towards Practical Real-Time Neural Video Compression](https://mlsanity.com/paper/2502.20762/) (2025)
- [MobileNVC: Real-time 1080p Neural Video Compression on a Mobile Device](https://mlsanity.com/paper/2310.01258/) (2023)
- [Device Interoperability for Learned Image Compression with Weights and Activations Quantization](https://mlsanity.com/paper/2212.01330/) (2022)
- [Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression](https://mlsanity.com/paper/2211.02854/) (2022)
- [Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information](https://mlsanity.com/paper/2309.11276/) (2023)
- [Generalized Gaussian Model for Learned Image Compression](https://mlsanity.com/paper/2411.19320/) (2024)
- [Evaluating the Practicality of Learned Image Compression](https://mlsanity.com/paper/2207.14524/) (2022)
- [Survey on Visual Signal Coding and Processing with Generative Models: Technologies, Standards and Optimization](https://mlsanity.com/paper/2405.14221/) (2024)
- [Effortless Cross-Platform Video Codec: A Codebook-Based Method](https://mlsanity.com/paper/2310.10292/) (2023)
- [MLVC: Multi-platform Learned Video Codec for Real-World Deployment](https://mlsanity.com/paper/2606.28027/) (2026)
- [A Multi-Stage Optimization Framework for Deploying Learned Image Compression on FPGAs](https://mlsanity.com/paper/2511.17135/) (2025)
- [Hadamard-Domain Model Quantization for Learned Image Coding](https://mlsanity.com/paper/2608.01653/) (2026)
- [HAMP-LIC: Hessian-Aware Mixed-Precision Post-Training Quantization for Learned Image Compression](https://mlsanity.com/paper/2608.12239/) (2026)
- [PULSE: Unlocking Practical Image Compression on Single-Thread CPU](https://mlsanity.com/paper/2609.18602/) (2026)

## Abstract (arXiv)

It has been witnessed that learned image compression has outperformed conventional image coding techniques and tends to be practical in industrial applications. One of the most critical issues that need to be considered is the non-deterministic calculation, which makes the probability prediction cross-platform inconsistent and frustrates successful decoding. We propose to solve this problem by introducing well-developed post-training quantization and making the model inference integer-arithmetic-only, which is much simpler than presently existing training and fine-tuning based approaches yet still keeps the superior rate-distortion performance of learned image compression. Based on that, we further improve the discretization of the entropy parameters and extend the deterministic inference to fit Gaussian mixture models. With our proposed methods, the current state-of-the-art image compression models can infer in a cross-platform consistent manner, which makes the further development and practice of learned image compression more promising.