Content-Aware Retargeted Image Quality Assessment
AbstractIn 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
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Zhang, T.; Yu, M.; Guo, Y.; Liu, Y. Content-Aware Retargeted Image Quality Assessment. Information 2019, 10, 111.
Zhang T, Yu M, Guo Y, Liu Y. Content-Aware Retargeted Image Quality Assessment. Information. 2019; 10(3):111.Chicago/Turabian Style
Zhang, Tingting; Yu, Ming; Guo, Yingchun; Liu, Yi. 2019. "Content-Aware Retargeted Image Quality Assessment." Information 10, no. 3: 111.
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