Image retargeting quality assessment: A survey

Author:

Guo Yingchun1,Wang Dan1,Yan Gang1,Zhu Ye1

Affiliation:

1. School of Artificial Intelligence, Hebei University of Technology, Tianjin, China

Abstract

With the increasing variety of display devices, image retargeting has become an indispensable technology for adjusting the aspect ratio of images to adapt to different display terminals. Since the retargeting operation would cause geometric distortion and content loss of the image, the image retargeting quality assessment (IRQA) is necessary to guide the retargeting algorithm’s optimization, selection, and design. Our paper mainly works for systematically reviewing the state-of-the-art technologies in IRQA. And then, this paper further discusses image registration algorithms for matching the original image and the retargeted image. Next, we investigate the feature measurement methods for image retargeting quality evaluation. To facilitate the quantitative assessment of the IRQA methods, this paper gives a list of publicly open datasets and the performance of the mainstream methods. Finally, some promising research directions towards IRQA are pointed out. From this survey, engineers from the industry may find skills to improve their image retargeting systems, and researchers from academia may find ideas to conduct some innovative work.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference56 articles.

1. Weakly supervised reinforced multi-operator image retargeting;Zhou;IEEE Transactions on Circuits and Systems for Video Technology,2021

2. Context-aware saliency detection for image retargeting using convolutional neural networks;Ahmadi;Multimedia Tools and Applications,2021

3. Image resizing by reconstruction from deep features;Danon;Computational Visual Media,2021

4. Image retargeting quality assessment based on registration confidence measure and noticeability-based pooling;Niu;IEEE Transactions on Circuits and Systems for Video Technology,2021

5. Color and texture descriptors;Manjunath;IEEE Transactions on Circuits and Systems for Video Technology,2001

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Survey on Content-aware Image Retargeting Techniques for Visually Impaired Assistance;2023 Eleventh International Conference on Intelligent Computing and Information Systems (ICICIS);2023-11-21

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3