Quality assessment of retargeted images by salient region deformity analysis

Displaying images on different devices, requires resizing of the media. Traditional image resizing methods result in quality degradation. Content-aware retargeting algorithms aim to resize images for displaying them on a new device with the goal of preserving important contents of the image. Quality assessment of retargeted images can be employed to choose among outputs of different retargeting methods or help the optimization of such methods. In this paper we propose a learning based quality assessment method for retargeted images. An optical flow algorithm is used to find the correspondence between regions in the scaled and retargeted images. Three groups of features are defined to cover different aspects of distortions that are important to human observers. Area related features are used to detect how the areas of salient regions are retained and how much geometrical deformities are produced in the image. Also, to better assess the retargeted image we introduce features to show how well the aspect ratios of objects are retained. More importantly, we introduce the concept of measuring the homogeneity of distribution of deformities throughout the image. Experimental results demonstrate that our quality estimation method has better correlation with subjective scores and outperforms existing methods.