摘要翻译：
人的视觉系统在感知图像时会注意显著区域。当观看立体3D(S3D)图像时，我们假设虽然对显著性的大部分贡献是由2D图像提供的，但深度分量提供了一个小但显著的贡献。此外，我们声称只有图像边缘的子集有助于在观看S3D图像时的深度感知。在本文中，我们提出了一种系统的深度显著性估计方法，称为相对于深度感知的显著性边缘(SED)，它对S3D图像中的深度显著性边缘进行局部化。我们演示了SED在全参考立体图像质量评估(FRSIQA)中的应用。我们考虑梯度幅值和梯度间图来预测结构相似性。首先，通过比较参考和测试立体对的二维显著性和梯度图，导出了一个粗略的质量估计。我们使用SED映射来改进这种质量，以评估深度质量。最后，我们结合这个亮度和深度质量来获得一个整体的立体图像质量。我们在七个公开的S3D IQA数据库上对我们的度量进行了全面的评估。拟议的指标显示了所有七个数据库的竞争业绩，其中三个数据库的最先进业绩。
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英文标题：
《Estimating Depth-Salient Edges And its Application To Stereoscopic Image
  Quality Assessment》
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作者：
Sameeulla Khan Md, Sumohana Channappayya
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  The human visual system pays attention to salient regions while perceiving an image. When viewing a stereoscopic 3D (S3D) image, we hypothesize that while most of the contribution to saliency is provided by the 2D image, a small but significant contribution is provided by the depth component. Further, we claim that only a subset of image edges contribute to depth perception while viewing an S3D image. In this paper, we propose a systematic approach for depth saliency estimation, called Salient Edges with respect to Depth perception (SED) which localizes the depth-salient edges in an S3D image. We demonstrate the utility of SED in full reference stereoscopic image quality assessment (FRSIQA). We consider gradient magnitude and inter-gradient maps for predicting structural similarity. A coarse quality estimate is derived first by comparing the 2D saliency and gradient maps of reference and test stereo pairs. We refine this quality using SED maps for evaluating depth quality. Finally, we combine this luminance and depth quality to obtain an overall stereo image quality. We perform a comprehensive evaluation of our metric on seven publicly available S3D IQA databases. The proposed metric shows competitive performance on all seven databases with state-of-the-art performance on three of them.
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PDF链接：
https://arxiv.org/pdf/1802.03883