# IQNet: Image Quality Assessment Guided Just Noticeable Difference Prefiltering For Versatile Video Coding

arXiv: https://arxiv.org/abs/2312.09799 · HTML: https://mlsanity.com/paper/2312.09799/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [Efficient Image Super-Resolution Using Pixel Attention](https://mlsanity.com/paper/2010.01073/) (2020)
- [VideoSet: A Large-Scale Compressed Video Quality Dataset Based on JND Measurement](https://mlsanity.com/paper/1701.01500/) (2017)
- [How to Exploit the Transferability of Learned Image Compression to Conventional Codecs](https://mlsanity.com/paper/2012.01874/) (2020)
- [A Dual-Critic Reinforcement Learning Framework for Frame-level Bit Allocation in HEVC/H.265](https://mlsanity.com/paper/2104.01735/) (2021)

## Built on by (most cited first, 1 of 1)

- [JND-Guided Light-Weight Neural Pre-Filter for Perceptual Image Coding](https://mlsanity.com/paper/2510.10648/) (2025)

## Abstract (arXiv)

Image prefiltering with just noticeable distortion (JND) improves coding efficiency in a visual lossless way by filtering the perceptually redundant information prior to compression. However, real JND cannot be well modeled with inaccurate masking equations in traditional approaches or image-level subject tests in deep learning approaches. Thus, this paper proposes a fine-grained JND prefiltering dataset guided by image quality assessment for accurate block-level JND modeling. The dataset is constructed from decoded images to include coding effects and is also perceptually enhanced with block overlap and edge preservation. Furthermore, based on this dataset, we propose a lightweight JND prefiltering network, IQNet, which can be applied directly to different quantization cases with the same model and only needs 3K parameters. The experimental results show that the proposed approach to Versatile Video Coding could yield maximum/average bitrate savings of 41%/15% and 53%/19% for all-intra and low-delay P configurations, respectively, with negligible subjective quality loss. Our method demonstrates higher perceptual quality and a model size that is an order of magnitude smaller than previous deep learning methods.