An Efficient Importance Map for Content Aware Image Resizing

Recently, content aware image resizing methods were proposed to supplement content oblivious methods such as scaling or cropping. A content aware resizing operator relies on an importance map to preserve the important parts of the image at the expense of the less-important ones. The importance map is an image-based measure and is a core part of the resizing algorithm. Importance measures include image gradients, saliency and entropy, as well as high level cues such as face detectors, motion detectors and more. In this work we proposed a new method to calculate the importance map, the importance map is generated automatically using a novel combination of image edge density and Harel saliency measurement. Experiments of different type images demonstrate that our method effectively detects prominent areas can be used in image resizing applications to aware importance important areas while preserving image quality.

Keywords:

Image resizing , resizing algorithm , image edge density and Harel saliency measurement