A bi-directional evaluation-based approach for image retargeting quality assessment

摘要

Image retargeting is a technique that adjusts input images into arbitrary dimensions (rows and columns) and simultaneously preserves regions of interest. Assess the image quality under varying aspect ratio is significantly more challenging since it requires content matching in addition to semantic content analysis. In this work, we propose an objective quality assessment algorithm for image retargeting, called bi-directional importance map similarity (BIMS). The key step in our approach is to assess quality in image retargeting through some features in a bi-directional way, all in a feature fusion framework. The motivation behind employing bi-directional features is because the nature of them is useful to estimate pertinent locations where we can analyze whenever relevant content is missing or any visual distortion arises. Our proposal was assessed on a well-known state-of-the-art dataset in which human viewers provided their personal opinions on the perceptual quality. Due to the experimental results obtained, we consider the BIMS is a good choice for quality assessment of retargeted images.