Rank Learning Based No-Reference Quality Assessment of Retargeted Images

Lin Ma, Long Xu, Yichi Zhang, King Ngi Ngan, Yihua Yan

Abstract: In 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.