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Information 2019, 10(3), 111; https://doi.org/10.3390/info10030111

Content-Aware Retargeted Image Quality Assessment

1
School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China
2
School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
*
Author to whom correspondence should be addressed.
Received: 16 January 2019 / Revised: 22 February 2019 / Accepted: 5 March 2019 / Published: 12 March 2019
(This article belongs to the Section Information Processes)
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Abstract

In targeting the low correlation between existing image scaling quality assessment methods and subjective awareness, a content-aware retargeted image quality assessment algorithm is proposed, which is based on the structural similarity index. In this paper, a similarity index, that is, a local structural similarity algorithm, which can measure different sizes of the same image is proposed. The Speed Up Robust Feature (SURF) algorithm is used to extract the local structural similarity and the image content loss degree. The significant area ratio is calculated by extracting the saliency region and the retargeted image quality assessment function is obtained by linear fusion. In the CUHK image database and the MIT RetargetMe database, compared with four representative assessment algorithms and other latest four kinds of retargeted image quality assessment algorithms, the experiment proves that the proposed algorithm has a higher correlation with Mean Opinion Score (MOS) values and corresponds with the result of human subjective assessment. View Full-Text
Keywords: content aware; image retarget; content-aware image scaling; image quality assessment; structural similarity content aware; image retarget; content-aware image scaling; image quality assessment; structural similarity
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Zhang, T.; Yu, M.; Guo, Y.; Liu, Y. Content-Aware Retargeted Image Quality Assessment. Information 2019, 10, 111.

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