Fast Hybrid Image Retargeting
Daniel Valdez-Balderas, Oleg Muraveynyk, Timothy Smith

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.…
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