Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140107
Title: Learning sparse representation for objective image retargeting quality assessment
Authors: Jiang, Qiuping
Shao, Feng
Lin, Weisi
Jiang, Gangyi
Keywords: Engineering::Computer science and engineering
Issue Date: 2017
Source: Jiang, Q., Shao, F., Lin, W., & Jiang, G. (2018). Learning sparse representation for objective image retargeting quality assessment. IEEE Transactions on Cybernetics, 48(4), 1276-1289. doi:10.1109/TCYB.2017.2690452
Journal: IEEE Transactions on Cybernetics
Abstract: The goal of image retargeting is to adapt source images to target displays with different sizes and aspect ratios. Different retargeting operators create different retargeted images, and a key problem is to evaluate the performance of each retargeting operator. Subjective evaluation is most reliable, but it is cumbersome and labor-consuming, and more importantly, it is hard to be embedded into online optimization systems. This paper focuses on exploring the effectiveness of sparse representation for objective image retargeting quality assessment. The principle idea is to extract distortion sensitive features from one image (e.g., retargeted image) and further investigate how many of these features are preserved or changed in another one (e.g., source image) to measure the perceptual similarity between them. To create a compact and robust feature representation, we learn two overcomplete dictionaries to represent the distortion sensitive features of an image. Features including local geometric structure and global context information are both addressed in the proposed framework. The intrinsic discriminative power of sparse representation is then exploited to measure the similarity between the source and retargeted images. Finally, individual quality scores are fused into an overall quality by a typical regression method. Experimental results on several databases have demonstrated the superiority of the proposed method.
URI: https://hdl.handle.net/10356/140107
ISSN: 2168-2275
DOI: 10.1109/TCYB.2017.2690452
Rights: © 2017 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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