Chen Fen, Chen Jiali, Peng Zongju, Jiang Gangyi, Yu Mei, Chen Hua, Jiao Renzhi
Appl Opt. 2017 Oct 20;56(30):8547-8554. doi: 10.1364/AO.56.008547.
Quality prediction of virtual-views is important for free viewpoint video systems, and can be used as feedback to improve the performance of depth video coding and virtual-view rendering. In this paper, an efficient virtual-view peak signal to noise ratio (PSNR) prediction method is proposed. First, the effect of depth distortion on virtual-view quality is analyzed in detail, and a depth distortion tolerance (DDT) model that determines the DDT range is presented. Next, the DDT model is used to predict the virtual-view quality. Finally, a support vector machine (SVM) is utilized to train and obtain the virtual-view quality prediction model. Experimental results show that the Spearman's rank correlation coefficient and root mean square error between the actual PSNR and the predicted PSNR by DDT model are 0.8750 and 0.6137 on average, and by the SVM prediction model are 0.9109 and 0.5831. The computational complexity of the SVM method is lower than the DDT model and the state-of-the-art methods.
虚拟视图的质量预测对于自由视点视频系统很重要，并且可以用作反馈来提高深度视频编码和虚拟视图渲染的性能。本文提出了一种高效的虚拟视图峰值信噪比（PSNR）预测方法。首先，详细分析了深度失真对虚拟视图质量的影响，并提出了一种确定深度失真容限（DDT）范围的深度失真容限模型。其次，利用DDT模型预测虚拟视图质量。最后，利用支持向量机（SVM）训练并获得虚拟视图质量预测模型。实验结果表明，DDT模型预测的实际PSNR与预测PSNR之间的斯皮尔曼等级相关系数和均方根误差平均分别为0.8750和0.6137，SVM预测模型的分别为0.9109和0.5831。SVM方法的计算复杂度低于DDT模型和现有方法。
