360-degree/omnidirectional images (OIs) have achieved remarkable attentions due to the increasing applications of virtual reality (VR). Compared to conventional 2D images, OIs can provide more immersive experience to consumers, benefitting from the higher resolution and plentiful field of views (FoVs). Moreover, observing OIs is usually in the head mounted display (HMD) without references. Therefore, an efficient blind quality assessment method, which is specifically designed for 360-degree images, is urgently desired. In this paper, motivated by the characteristics of the human visual system (HVS) and the viewing process of VR visual contents, we propose a novel and effective no-reference omnidirectional image quality assessment (NR OIQA) algorithm by Multi-Frequency Information and Local-Global Naturalness (MFILGN). Specifically, inspired by the frequency-dependent property of visual cortex, we first decompose the projected equirectangular projection (ERP) maps into wavelet subbands. Then, the entropy intensities of low and high frequency subbands are exploited to measure the multi-frequency information of OIs. Besides, except for considering the global naturalness of ERP maps, owing to the browsed FoVs, we extract the natural scene statistics features from each viewport image as the measure of local naturalness. With the proposed multi-frequency information measurement and local-global naturalness measurement, we utilize support vector regression as the final image quality regressor to train the quality evaluation model from visual quality-related features to human ratings. To our knowledge, the proposed model is the first no-reference quality assessment method for 360-degreee images that combines multi-frequency information and image naturalness. Experimental results on two publicly available OIQA databases demonstrate that our proposed MFILGN outperforms state-of-the-art approaches.

中文翻译：

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通过多频信息和局部全球自然性分析对360度图像进行无参考质量评估

由于虚拟现实（VR）的应用越来越多，因此360度/全向图像（OI）受到了极大的关注。与传统的2D图像相比，OI可以受益于更高的分辨率和丰富的视场（FoV），从而为消费者提供更身临其境的体验。此外，观察OI通常在没有参考的头戴式显示器（HMD）中。因此，迫切需要一种专门针对360度图像设计的有效盲质量评估方法。本文基于人类视觉系统（HVS）的特点和VR视觉内容的观看过程，提出了一种新颖有效的基于多频信息和局部的无参考全向图像质量评估（NR OIQA）算法。 -全球自然（MFILGN）。具体来说，受视觉皮层频率相关属性的启发，我们首先将投影的等角矩形投影（ERP）映射分解为小波子带。然后，利用低频子带和高频子带的熵强度来测量OI的多频信息。此外，除了考虑ERP地图的全局自然性之外，由于浏览了FoV，我们从每个视口图像中提取自然场景统计特征作为局部自然性的度量。通过提出的多频信息测量和局部全局自然性测量，我们利用支持向量回归作为最终的图像质量回归指标来训练从视觉质量相关特征到人类等级的质量评估模型。据我们所知，该模型是结合多频信息和图像自然性的360度图像无参考质量评估方法。在两个可公开获得的OIQA数据库上的实验结果表明，我们提出的MFILGN优于最新的方法。