Fast synthesized and predicted just noticeable distortion maps for perceptual multiview video coding

The just noticeable distortion (JND) map is a useful tool for perceptual video coding. However, direct calculation of the JND map incurs high complexity, and the problem is aggravated in multiview video coding. In this paper, two fast methods are proposed to generate the JND maps of multiview videos. In the first method, the JND maps of some anchor views are used to synthesize the JND maps of other views via the depth image based rendering (DIBR), which can be much faster than direct JND computation. In the second method, the motion and disparity vectors obtained during the video coding are employed to predict the JND maps. If the prediction is not satisfactory, the JND block will be refreshed by calculating the JND directly. This method does not need any camera parameters and depth maps. The performances of the two fast JND map generation methods are evaluated in a perceptual MVC framework, where the residuals after spatial, temporal, or inter-view prediction are tuned according to the JND thresholds to save the bits without affecting the perceptual quality. Experimental results show that the JND prediction method has better accuracy and lower complexity. In addition, both fast JND methods lead to negligible degradation of the coding performance, compared to the direct JND method.

► We propose the synthesized and predicted just noticeable distortion (JND) maps for perceptual multiview video coding (MVC).
► We propose the synthesis of JND maps from anchor views.
► We propose the block-matching-based JND prediction.
► The JND prediction method has a better estimation of JND maps with lower computational complexity.
► The fast JND methods lead to negligible degradation of the coding performance, compared to direct JND method.