This paper proposes to blindly evaluate the quality of images synthesized via a depth image-based rendering (DIBR) procedure. As a significant branch of virtual reality (VR), superior DIBR techniques provide free viewpoints in many real applications, including remote surveillance and education; however, limited efforts have been made to measure the performance of DIBR techniques, or equivalently the quality of DIBR-synthesized views, especially in the condition when references are unavailable. To achieve this aim, we develop a novel blind image quality assessment (IQA) method via multiscale natural scene statistical analysis (MNSS). The design principle of our proposed MNSS metric is based on two new natural scene statistics (NSS) models specific to the DBIR-synthesized IQA. First, the DIBR-introduced geometric distortions damage the local self-similarity characteristic of natural images, and the damage degrees of self-similarity present particular variations at different scales. Systematically combining the measurements of the variations mentioned above can gauge the naturalness of the input image and thus indirectly reflect the quality changes of images generated using different DIBR methods. Second, it was found that the degradations in main structures of natural images at different scales remain almost the same, whereas the statistical regularity is destroyed in the DIBR-synthesized views. Estimating the deviation of degradations in main structures at different scales between one DIBR-synthesized image and the statistical model, which is constructed based on a large number of natural images, can quantify how a DIBR method damages the main structures and thus infer the image quality. Via trials, the two NSS-based features extracted above can well predict the quality of DIBR-synthesized images. Further, the two features come from distinct points of view, and we hence integrate them via a straightforward multiplication to derive the proposed blind MNSS metric, which achieves better performance than each component and state-of-the-art quality methods.

中文翻译：

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DIBR合成视图无参考质量评估的多尺度自然场景统计分析

本文建议通过基于深度图像的渲染 (DIBR) 程序盲目评估合成图像的质量。作为虚拟现实 (VR) 的一个重要分支，卓越的 DIBR 技术在许多实际应用中提供了自由视角，包括远程监控和教育；然而，在测量 DIBR 技术的性能或等效的 DIBR 合成视图的质量方面做出了有限的努力，尤其是在参考资料不可用的情况下。为了实现这一目标，我们通过多尺度自然场景统计分析（MNSS）开发了一种新的盲图像质量评估（IQA）方法。我们提出的 MNSS 度量的设计原则基于两个新的自然场景统计 (NSS) 模型，这些模型特定于 DBIR 合成的 IQA。第一的，DIBR引入的几何畸变破坏了自然图像的局部自相似性特征，自相似性的破坏程度在不同尺度上呈现出特定的变化。系统地结合上述变化的测量可以衡量输入图像的自然度，从而间接反映使用不同 DIBR 方法生成的图像的质量变化。其次，发现不同尺度的自然图像主要结构的退化几乎保持不变，而在DIBR合成视图中统计规律性被破坏。估计一张DIBR合成图像与基于大量自然图像构建的统计模型之间不同尺度的主要结构退化的偏差，可以量化 DIBR 方法如何破坏主要结构，从而推断图像质量。通过试验，上面提取的两个基于 NSS 的特征可以很好地预测 DIBR 合成图像的质量。此外，这两个特征来自不同的观点，因此我们通过简单的乘法将它们集成以推导出所提出的盲 MNSS 度量，该度量比每个组件和最先进的质量方法都具有更好的性能。