# Full RGB Just Noticeable Difference (JND) Modelling

arXiv: https://arxiv.org/abs/2203.00629 · HTML: https://mlsanity.com/paper/2203.00629/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 61 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 13 of 13)

- [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)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [A Simple Pooling-Based Design for Real-Time Salient Object Detection](https://mlsanity.com/paper/1904.09569/) (2019)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [VideoSet: A Large-Scale Compressed Video Quality Dataset Based on JND Measurement](https://mlsanity.com/paper/1701.01500/) (2017)
- [Just Noticeable Difference for Deep Machine Vision](https://mlsanity.com/paper/2102.08168/) (2021)
- [Performance Comparison of Convolutional AutoEncoders, Generative Adversarial Networks and Super-Resolution for Image Compression](https://mlsanity.com/paper/1807.00270/) (2018)
- [Analysis and prediction of JND-based video quality model](https://mlsanity.com/paper/1807.00681/) (2018)
- [A user model for JND-based video quality assessment: theory and applications](https://mlsanity.com/paper/1807.10894/) (2018)

## Built on by (most cited first, 4 of 4)

- [Minimum Noticeable Difference based Adversarial Privacy Preserving Image Generation](https://mlsanity.com/paper/2206.08638/) (2022)
- [JND-Based Perceptual Optimization For Learned Image Compression](https://mlsanity.com/paper/2302.13092/) (2023)
- [HVS-Inspired Signal Degradation Network for Just Noticeable Difference Estimation](https://mlsanity.com/paper/2208.07583/) (2022)
- [JNDMix: JND-Based Data Augmentation for No-reference Image Quality Assessment](https://mlsanity.com/paper/2302.09838/) (2023)

## Abstract (arXiv)

Just Noticeable Difference (JND) has many applications in multimedia signal processing, especially for visual data processing up to date. It's generally defined as the minimum visual content changes that the human can perspective, which has been studied for decades. However, most of the existing methods only focus on the luminance component of JND modelling and simply regard chrominance components as scaled versions of luminance. In this paper, we propose a JND model to generate the JND by taking the characteristics of full RGB channels into account, termed as the RGB-JND. To this end, an RGB-JND-NET is proposed, where the visual content in full RGB channels is used to extract features for JND generation. To supervise the JND generation, an adaptive image quality assessment combination (AIC) is developed. Besides, the RDB-JND-NET also takes the visual attention into account by automatically mining the underlying relationship between visual attention and the JND, which is further used to constrain the JND spatial distribution. To the best of our knowledge, this is the first work on careful investigation of JND modelling for full-color space. Experimental results demonstrate that the RGB-JND-NET model outperforms the relevant state-of-the-art JND models. Besides, the JND of the red and blue channels are larger than that of the green one according to the experimental results of the proposed model, which demonstrates that more changes can be tolerated in the red and blue channels, in line with the well-known fact that the human visual system is more sensitive to the green channel in comparison with the red and blue ones.