Quality Assessment of Image Retargeting based on Importance of Objects

Abstract

Many novel image retargeting algorithms have been proposed to adjust the size of images to suit different display devices while minimizing perceptual distortion. Assessing the quality of retargeted images has become an important task for developing such schemes. In this study, we propose an image retargeting quality assessment method based on the importance of objects in an image. We utilize semantic segmentation to classify pixels and assign them different importance values representing the sensitivity of human eyes to distortion. A visual saliency map is created to better match the subjective perception of humans and is then used in the "Aspect Ratio Similarity" measurement to improve its accuracy. Since human eyes tend to be more sensitive to the information loss in images without prominent foreground objects, we introduce an information loss adjustment strategy for such images. The experimental results demonstrate that the proposed method is effective in evaluating image retargeting algorithms and outperforms existing quality assessment methods.