Fast Hybrid Image Retargeting

• URL: http://arxiv.org/abs/2203.13595v1
• Date: Fri, 25 Mar 2022 11:46:06 GMT
• Title: Fast Hybrid Image Retargeting
• Authors: Daniel Valdez-Balderas, Oleg Muraveynyk, Timothy Smith
• Abstract summary: We propose a method that quantifies and limits warping distortions with the use of content-aware cropping. Our method outperforms recent approaches, while running in a fraction of their execution time.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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