Geometry-based spherical JND modeling for 360^ display

Abstract

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.

Tasks

Reproductions