Transformation-Aware Similarity Measurement for Image Retargeting Quality Assessment via Bidirectional Rewarping

Abstract

Image retargeting is an effective way to adapt images for target displays with different aspect ratios and sizes. Meanwhile, effective image retargeting quality assessment (IRQA) is important for optimizing the image retargeting operations. In this paper, we propose a transform-aware similarity (TRASIM) measurement metric for IRQA, including bidirectional geometric distortion measurement, bidirectional information loss measurement, and global salient structure distortion measurement. The main innovation of the TRASIM is to build a universal framework to establish the similarity transformation via bidirectional rewarping to simulate different types of retargeting operators. Based on the similarity transformation, geometric distortion and content loss are measured to determine the retargeting quality. Experimental results on two widely used databases (CUHK and RetargetMe) indicate that the proposed TRASIM has higher consistency with subjective ranks, compared with the state-of-the-art IRQA metrics.