A Comprehensive Framework For 2D-Jnd Extension To 360-Deg Images

Masking effect is one of the most important perceptual properties that could be modeled by estimating an adaptive threshold known as the just noticeable difference (JND) referring to the maximum difference not perceived by the human visual system (HVS). In this paper, a novel framework to extend 2D-JND models to estimate thresholds for 360-degree images is proposed. The JND is estimated by viewports instead of applying it to the projected format image. Then, the viewport-based JND maps are back-projected to obtain the 360-JND map. To reduce the visible boundaries between viewports an alpha blending process is applied. The validation of the proposed framework is made using subjective experiments. It demonstrates that when applied to 360-degree images, the proposed framework outperforms the 2D-JND models in terms of observers preference at the same noise level.