Image retargeting via Beltrami representation

TL;DR

This paper introduces a simple, efficient image retargeting method using Beltrami representation to preserve important content geometry during resizing, avoiding complex optimization.

Contribution

The proposed approach employs Beltrami representation for image warping, enabling content-preserving resizing without optimization or parameter tuning.

Findings

Effective preservation of important content during resizing

No need for optimization or parameter tuning

Demonstrated efficiency and efficacy through extensive experiments

Abstract

Image retargeting aims to resize an image to one with a prescribed aspect ratio. Simple scaling inevitably introduces unnatural geometric distortions on the important content of the image. In this paper, we propose a simple and yet effective method to resize an image, which preserves the geometry of the important content, using the Beltrami representation. Our algorithm allows users to interactively label content regions as well as line structures. Image resizing can then be achieved by warping the image by an orientation-preserving bijective warping map with controlled distortion. The warping map is represented by its Beltrami representation, which captures the local geometric distortion of the map. By carefully prescribing the values of the Beltrami representation, images with different complexity can be effectively resized. Our method does not require solving any optimization problems and tuning parameters throughout the process. This results in a simple and efficient algorithm to solve the image retargeting problem. Extensive experiments have been carried out, which demonstrate the efficacy of our proposed method.