Depth-image-based rendering (DIBR) is widely used in 3DTV, free-viewpoint video, and interactive 3D graphics applications. Typically, synthetic images generated by DIBR-based systems incorporate various distortions, particularly geometric distortions induced by object dis-occlusion. Ensuring the quality of synthetic images is critical to maintaining adequate system service. However, traditional 2D image quality metrics are ineffective for evaluating synthetic images as they are not sensitive to geometric distortion. In this paper, we propose a novel no-reference image quality assessment method for synthetic images based on convolutional neural networks, introducing local image saliency as prediction weights. Due to the lack of existing training data, we construct a new DIBR synthetic image dataset as part of our contribution. Experiments were conducted on both the public benchmark IRCCyN/IVC DIBR image dataset and our own dataset. Results demonstrate that our proposed metric outperforms traditional 2D image quality metrics and state-of-the-art DIBR-related metrics.

中文翻译：

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卷积神经网络和局部图像显着性的无参考综合图像质量评估

基于深度图像的渲染（DIBR）广泛用于3DTV，自由视点视频和交互式3D图形应用程序中。通常，由基于DIBR的系统生成的合成图像会合并各种变形，特别是由对象遮挡引起的几何变形。确保合成图像的质量对于维持足够的系统服务至关重要。但是，传统的2D图像质量指标对合成图像不起作用，因为它们对几何变形不敏感。在本文中，我们提出了一种基于卷积神经网络的合成图像无参考图像质量评估方法，将局部图像显着性作为预测权重。由于缺乏现有的训练数据，我们构建了一个新的DIBR合成图像数据集，作为我们贡献的一部分。在公开基准IRCCyN / IVC DIBR图像数据集和我们自己的数据集上均进行了实验。结果表明，我们提出的指标优于传统的2D图像质量指标和最新的与DIBR相关的指标。