# JND-Based Perceptual Optimization For Learned Image Compression

arXiv: https://arxiv.org/abs/2302.13092 · HTML: https://mlsanity.com/paper/2302.13092/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 25 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [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)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [Just Noticeable Difference for Deep Machine Vision](https://mlsanity.com/paper/2102.08168/) (2021)
- [Towards Top-Down Just Noticeable Difference Estimation of Natural Images](https://mlsanity.com/paper/2108.05058/) (2021)
- [Full RGB Just Noticeable Difference (JND) Modelling](https://mlsanity.com/paper/2203.00629/) (2022)
- [HVS-Inspired Signal Degradation Network for Just Noticeable Difference Estimation](https://mlsanity.com/paper/2208.07583/) (2022)

## Built on by (most cited first, 3 of 3)

- [Customizable ROI-Based Deep Image Compression](https://mlsanity.com/paper/2507.00373/) (2025)
- [DT-JRD: Deep Transformer based Just Recognizable Difference Prediction Model for Video Coding for Machines](https://mlsanity.com/paper/2411.09308/) (2024)
- [Perceptual Learned Image Compression via End-to-End JND-Based Optimization](https://mlsanity.com/paper/2402.02836/) (2024)

## Abstract (arXiv)

Recently, learned image compression schemes have achieved remarkable improvements in image fidelity (e.g., PSNR and MS-SSIM) compared to conventional hybrid image coding ones due to their high-efficiency non-linear transform, end-to-end optimization frameworks, etc. However, few of them take the Just Noticeable Difference (JND) characteristic of the Human Visual System (HVS) into account and optimize learned image compression towards perceptual quality. To address this issue, a JND-based perceptual quality loss is proposed. Considering that the amounts of distortion in the compressed image at different training epochs under different Quantization Parameters (QPs) are different, we develop a distortion-aware adjustor. After combining them together, we can better assign the distortion in the compressed image with the guidance of JND to preserve the high perceptual quality. All these designs enable the proposed method to be flexibly applied to various learned image compression schemes with high scalability and plug-and-play advantages. Experimental results on the Kodak dataset demonstrate that the proposed method has led to better perceptual quality than the baseline model under the same bit rate.