Image retargeting using depth assisted saliency map

Abstract

Retargeting algorithms are used to transfer and display images on devices with various sizes and resolutions. All of these algorithms try to preserve the important parts of image against distortions while producing a retargeted image with visual quality comparable with the original one. The main challenge in different algorithms is to find a suitable energy function that properly estimates the importance of each pixel in image. Hence the energy map needs to be improved. In this paper we propose a novel energy function which combines the information from saliency map, depth map and gradient map. We also present an algorithm to adaptively assign proper weights to these three importance maps for each input image. Then we calculate a switching threshold based on energy map that determines when to apply seam carving or scaling. The idea is to use a combination of seam carving and scaling to preserve the structure of important parts of image against distortion when the image size decreases beyond a point. This method reduces shape deformations and visual artifacts in salient regions of images and produces better quality output images. The results of the proposed method show superior visual quality in produced images in comparison to the state-of-the-arts.