Just noticeable distortion map prediction for perceptual multiview video coding

Abstract

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, the motion and disparity vectors obtained during the video coding are employed to predict the JND maps in order to reduce the complexity. The error propagation of the prediction is studied and a JND block refreshing approach is proposed, when the prediction is not satisfactory, to alleviate the influence of the error propagation. The performance of the proposed JND prediction method is evaluated in a perceptual MVC framework, where the prediction residuals 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 than an existing JND synthesis method. In addition, the proposed method leads to negligible degradation of the coding performance, compared to the direct JND method.