Human Visual System-Based Saliency Detection for High Dynamic Range Content
Human Visual System-Based Saliency Detection for High Dynamic Range Content
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DOI：
10.1109/tmm.2016.2522639
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发表时间：
2016-01
影响因子：
7.3
通讯作者：
Yuanyuan Dong;M. Pourazad;P. Nasiopoulos
中科院分区：
文献类型：
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作者：
Yuanyuan Dong;M. Pourazad;P. Nasiopoulos
人类视觉系统（HVS）试图选择显著区域以减少认知处理努力。视觉注意力的计算模型试图预测人眼观看的视频或图像中最相关和最重要的区域。这样的模型，反过来，可以应用于计算机图形学，视频编码和质量评估等领域。虽然已经提出了几种模型，但其中只有一种适用于高动态范围（HDR）图像内容，并且还没有针对HDR视频进行工作。此外，现有模型的主要缺点是它们不能模拟HDR内容中发现的宽发光范围下的HVS特性。本文通过提出一种计算方法来解决这些问题，该方法通过结合空间和时间视觉特征来对HDR输入的自下而上的视觉显著性进行建模。眼动数据的分析肯定了所提出的模型的有效性。采用三个著名的定量指标的比较表明，该模型大大提高了视觉注意力的HDR内容的预测。
The human visual system (HVS) attempts to select salient areas to reduce cognitive processing efforts. Computational models of visual attention try to predict the most relevant and important areas of videos or images viewed by the human eye. Such models, in turn, can be applied to areas such as computer graphics, video coding, and quality assessment. Although several models have been proposed, only one of them is applicable to high dynamic range (HDR) image content, and no work has been done for HDR videos. Moreover, the main shortcoming of the existing models is that they cannot simulate the characteristics of HVS under the wide luminous range found in HDR content. This paper addresses these issues by presenting a computational approach to model the bottom-up visual saliency for HDR input by combining spatial and temporal visual features. An analysis of eye movement data affirms the effectiveness of the proposed model. Comparisons employing three well-known quantitative metrics show that the proposed model substantially improves predictions of visual attention for HDR content.