Objective Quality Assessment for Image Retargeting Based on Perceptual Geometric Distortion and Information Loss

Abstract

Image retargeting techniques aim to obtain retargeted images with different sizes or aspect ratios for various display screens. Various content-aware image retargeting algorithms have been proposed recently. However, there is still no effective objective metric for visual quality assessment of retargeted images. In this paper, we propose a novel full-reference objective metric for assessing visual quality of a retargeted image based on perceptual geometric distortion and information loss. The proposed metric measures the geometric distortion of a retargeted image based on the local variance of SIFT flow vector fields of the image. Furthermore, a visual saliency map is derived to characterize human perception of the geometric distortion. Besides, the information loss in the retargeted image, which is estimated based on the saliency map, is also taken into account in the proposed metric. Subjective tests are conducted to evaluate the performance of the proposed metric. Our experimental results show the good consistency between the proposed objective metric and the subjective rankings.