Learning quality assessment of retargeted images

作者：

Highlights：

• Propose an open framework for image retargeting quality assessment.

• Embody a novel image retargeting quality assessment model which combines CW-SSIM, SIFT and image saliency.

• Embody a novel no-reference image aesthetics quality assessment method for retargeted images.

摘要

•Propose an open framework for image retargeting quality assessment.•Embody a novel image retargeting quality assessment model which combines CW-SSIM, SIFT and image saliency.•Embody a novel no-reference image aesthetics quality assessment method for retargeted images.

论文关键词：Image retargeting,Machine learning,RBF neural network,CW-SSIM,SIFT,Image aesthetics

论文评审过程：Received 28 May 2016, Revised 6 April 2017, Accepted 6 April 2017, Available online 9 April 2017, Version of Record 25 April 2017.

论文官网地址：https://doi.org/10.1016/j.image.2017.04.005