Image Retargeting Quality Assessment

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dc.contributor.authorLiu, Yong-Jinen_US
dc.contributor.authorLuo, Xien_US
dc.contributor.authorXuan, Yu-Mingen_US
dc.contributor.authorChen, Wen-Fengen_US
dc.contributor.authorFu, Xiao-Lanen_US
dc.contributor.editorM. Chen and O. Deussenen_US
dc.date.accessioned2015-02-27T10:23:26Z
dc.date.available2015-02-27T10:23:26Z
dc.date.issued2011en_US
dc.description.abstractContent-aware image retargeting is a technique that can flexibly display images with different aspect ratios and simultaneously preserve salient regions in images. Recently many image retargeting techniques have been proposed. To compare image quality by different retargeting methods fast and reliably, an objective metric simulating the human vision system (HVS) is presented in this paper. Different from traditional objective assessment methods that work in bottom-up manner (i.e., assembling pixel-level features in a local-to-global way), in this paper we propose to use a reverse order (top-down manner) that organizes image features from global to local viewpoints, leading to a new objective assessment metric for retargeted images. A scale-space matching method is designed to facilitate extraction of global geometric structures from retargeted images. By traversing the scale space from coarse to fine levels, local pixel correspondence is also established. The objective assessment metric is then based on both global geometric structures and local pixel correspondence. To evaluate color images, CIE L*a*b* color space is utilized. Experimental results are obtained to measure the performance of objective assessments with the proposed metric. The results show good consistency between the proposed objective metric and subjective assessment by human observers.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.doi10.1111/j.1467-8659.2011.01881.xen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2011.01881.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleImage Retargeting Quality Assessmenten_US
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