Fast Hybrid Image Retargeting

TL;DR

This paper introduces a fast, content-aware image retargeting method that minimizes distortion by combining semantic segmentation, saliency detection, and cropping, outperforming recent approaches in quality and speed.

Contribution

The proposed approach uniquely integrates deep semantic segmentation with content-aware cropping to achieve high-quality, fast image retargeting with limited warping distortions.

Findings

Outperforms recent retargeting methods in quality

Runs significantly faster than comparable approaches

Effective in preserving important content with minimal distortion

Abstract

Image retargeting changes the aspect ratio of images while aiming to preserve content and minimise noticeable distortion. Fast and high-quality methods are particularly relevant at present, due to the large variety of image and display aspect ratios. We propose a retargeting method that quantifies and limits warping distortions with the use of content-aware cropping. The pipeline of the proposed approach consists of the following steps. First, an importance map of a source image is generated using deep semantic segmentation and saliency detection models. Then, a preliminary warping mesh is computed using axis aligned deformations, enhanced with the use of a distortion measure to ensure low warping deformations. Finally, the retargeted image is produced using a content-aware cropping algorithm. In order to evaluate our method, we perform a user study based on the RetargetMe benchmark. Experimental analyses show that our method outperforms recent approaches, while running in a fraction of their execution time.