Rank Learning Based No-Reference Quality Assessment of Retargeted Images
Refereed conference paper presented and published in conference proceedings


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AbstractIn this paper, we first propose a novel no-reference (NR) image quality assessment (IQA) method for retargeted image based on the rank learning approach. Firstly, image features for each retargeted image are extracted, which should not only represent the image characteristics but also be sensitive to the retargeted distortions. Specifically, the image feature should be able to capture the shape distortions, which are the commonly encountered distortions of the retargeted image. Based on the extracted image features, the rank learning method is employed to train a model to discriminate the perceptual quality of the retargeted image. Experimental results demonstrate that the proposed method can effectively depict the perceptual quality of the retargeted image, which can even perform comparably with the full-reference (FR) quality assessment methods.
All Author(s) ListMa L., Xu L., Zhang Y., Ngan K.N., Yan Y.
Name of ConferenceIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
Start Date of Conference09/10/2015
End Date of Conference12/10/2015
Place of ConferenceKowloon Tong
Country/Region of ConferenceHong Kong
Detailed descriptionorganized by IEEE,
Year2016
Month1
Day12
Pages1023 - 1028
ISBN9781479986965
ISSN1062-922X
LanguagesEnglish-United Kingdom
Keywordsimage quality assessment (IQA), no-refernce (NR), rank learning, Retargeted image

Last updated on 2020-21-11 at 02:01