No-Reference Retargeted Image Quality Assessment Based on Pairwise Rank Learning

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

Abstract: In this paper, we propose a novel no-reference image quality assessment method for the retargeted image based on the pairwise rank learning approach. Each retargeted image needs to be first represented as a feature vector, which not only captures the image characteristics but also is sensitive to distortions during the retargeting process. As such, we investigate and examine different image representations for their abilities depicting the perceptual quality of retargeted image. Based on the image representations, we resort to the pairwise rank learning approach to discriminate the perceptual quality between the retargeted image pairs. 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 quality assessment methods.