Learning sparse representation for objective image retargeting quality assessment

Abstract

The goal of image retargeting is to adapt source images to target displays with different sizes and aspect ratios. Different retargeting operators create different retargeted images, and a key problem is to evaluate the performance of each retargeting operator. Subjective evaluation is most reliable, but it is cumbersome and labor-consuming, and more importantly, it is hard to be embedded into online optimization systems. This paper focuses on exploring the effectiveness of sparse representation for objective image retargeting quality assessment. The principle idea is to extract distortion sensitive features from one image (e.g., retargeted image) and further investigate how many of these features are preserved or changed in another one (e.g., source image) to measure the perceptual similarity between them. To create a compact and robust feature representation, we learn two overcomplete dictionaries to represent the distortion sensitive features of an image. Features including local geometric structure and global context information are both addressed in the proposed framework. The intrinsic discriminative power of sparse representation is then exploited to measure the similarity between the source and retargeted images. Finally, individual quality scores are fused into an overall quality by a typical regression method. Experimental results on several databases have demonstrated the superiority of the proposed method.