# Just Noticeable Visual Redundancy Forecasting: A Deep Multimodal-driven Approach

arXiv: https://arxiv.org/abs/2303.10372 · HTML: https://mlsanity.com/paper/2303.10372/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 18 Mar 2023.

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

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform](https://mlsanity.com/paper/1804.02815/) (2018)
- [ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph](https://mlsanity.com/paper/2006.16934/) (2020)
- [Towards Top-Down Just Noticeable Difference Estimation of Natural Images](https://mlsanity.com/paper/2108.05058/) (2021)

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

- [Multi-task Just Recognizable Difference for Video Coding for Machines: Database, Model, and Coding Application](https://mlsanity.com/paper/2604.09421/) (2026)

## Abstract (arXiv)

Just noticeable difference (JND) refers to the maximum visual change that human eyes cannot perceive, and it has a wide range of applications in multimedia systems. However, most existing JND approaches only focus on a single modality, and rarely consider the complementary effects of multimodal information. In this article, we investigate the JND modeling from an end-to-end homologous multimodal perspective, namely hmJND-Net. Specifically, we explore three important visually sensitive modalities, including saliency, depth, and segmentation. To better utilize homologous multimodal information, we establish an effective fusion method via summation enhancement and subtractive offset, and align homologous multimodal features based on a self-attention driven encoder-decoder paradigm. Extensive experimental results on eight different benchmark datasets validate the superiority of our hmJND-Net over eight representative methods.