360∘ videos have received widespread attention due to its realistic and immersive experiences for users. To date, how to accurately model the user perceptions on 360∘ display is still a challenging issue. In this paper, we exploit the visual characteristics of 360∘ projection and display and extend the popular just noticeable difference (JND) model to spherical JND (SJND). First, we propose a quantitative 2D-JND model by jointly considering spatial contrast sensitivity, luminance adaptation and texture masking effect. In particular, our model introduces an entropy-based region classification and utilizes different parameters for different types of regions for better modeling performance. Second, we extend our 2D-JND model to SJND by jointly exploiting latitude projection and field of view during 360∘ display. With this operation, SJND reflects both the characteristics of human vision system and the 360∘ display. Third, our SJND model is more consistent with user perceptions during subjective test and also shows more tolerance in distortions with fewer bit rates during 360∘ video compression. To further examine the effectiveness of our SJND model, we embed it in Versatile Video Coding (VVC) compression. Compared with the state-of-the-arts, our SJND-VVC framework significantly reduced the bit rate with negligible loss in visual quality.
@article{arxiv.2303.03703,
title = {Geometry-based spherical JND modeling for 360$^\circ$ display},
author = {Hongan Wei and Jiaqi Liu and Bo Chen and Liqun Lin and Weiling Chen and Tiesong Zhao},
journal= {arXiv preprint arXiv:2303.03703},
year = {2023}
}