Image retargeting using a bandelet-based similarity measure

Abstract

Media content retargeting aims to adapt images/ videos to displays of large or small sizes. In this work, we propose a bandelet-based image retargeting algorithm for summarizing image data into smaller sizes. First, we define a multi-scale bandelet-based perceptual similarity measure which measures the geometric and perceptual similarities between two images at different bandelet scales. Two images are said to be geometrically similar if they have approximately the same geometric flow and quadtree structure. After determining the geometric similarity, a perceptual similarity measure based on the properties of the human visual system is defined to assess the perceptual difference between the original image and the retargeted one. Then, the problem of image retargeting is considered as a geometric optimization problem based on the bandelet-based geometric and perceptual similarity measures. That is, for an image S we search for a retargeted image T that contains as much as possible of geometric and perceptual information from S and, consequently, preserves visual coherence. The proposed retargeting algorithm outperforms the state-of-the-art methods in terms of the visual quality of the retargeted image.