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首页> 外文期刊>Journal of visual communication & image representation >Fast synthesized and predicted just noticeable distortion maps for perceptual multiview video coding
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Fast synthesized and predicted just noticeable distortion maps for perceptual multiview video coding

机译：快速合成和预测的可感知多视点视频编码的明显失真图

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摘要

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.
机译：刚明显的失真（JND）映射是用于感知视频编码的有用工具。然而，直接计​​算JND图会导致高复杂度，并且在多视点视频编码中该问题更加严重。本文提出了两种快速的方法来生成多视图视频的JND地图。在第一种方法中，一些锚视图的JND映射用于通过基于深度图像的渲染（DIBR）来合成其他视图的JND映射，这比直接JND计算要快得多。在第二种方法中，采用在视频编码期间获得的运动和视差矢量来预测JND映射。如果预测不令人满意，则将通过直接计算JND来刷新JND块。此方法不需要任何相机参数和深度图。在感知MVC框架中评估了两种快速JND映射生成方法的性能，在该框架中，根据JND阈值调整了空间，时间或视图间预测后的残差，以节省比特而不影响感知质量。实验结果表明，JND预测方法具有较高的准确性和较低的复杂度。此外，与直接JND方法相比，这两种快速JND方法都导致编码性能的降低可忽略不计。

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