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IEEE Final Year Project Topic for IT

Base Paper Title

Estimating Depth-Salient Edges And Its Application To Stereoscopic Image Quality Assessment

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IEEE Project Abstract

The human visual system pays attention to salient regions while perceiving an image. When viewing a stereoscopic 3-D (S3D) image, we hypothesize that while most of the contribution to saliency is provided by the 2-D 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. We consider gradient magnitude and inter-gradient maps for predicting structural similarity. A coarse quality map is estimated first by comparing the 2-D saliency and gradient maps of reference and test stereo pairs. We average this quality map to estimate luminance quality and refine this quality map 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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