Registration based retargeted image quality assessment

Bo Zhang, Pedro V. Sander, Amine Bermak

Abstract: In recent years, a large number of image retargeting methods have been proposed. Measuring their relative quality is of significant importance, and there is still room for improvement in the effectiveness of objective retargeted image quality assessment (RIQA) metrics. In this paper, we propose a registration based RIQA metric. First, we propose to calculate the flow map using an image registration method which involves SURF point matching and halfway domain optimization. Using the computed flow map and the source image, we propose an LGI metric which contains three factors: 1) local similarity which assesses the local aspect ratio change, edge directional similarity and flow smoothness; 2) global distortion which measures the appearance change of salient objects; 3) salient information loss. Comparing with other six metrics, our LGI metric correlates the best with subjective rankings on the RetargetMe dataset.