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2. School of Computer Science and Engineering, Beihang University, Beijing 100191, China;
3. Sense Time, Beijing 100084, China, fund=null, authors=JIN Xin1 , ZHOU Bin2 , ZOU Dongqing3 , LI Xiaodong1 , SUN Hongbo1 , WU Le1 , authorsList=JIN Xin, ZHOU Bin, ZOU Dongqing, LI Xiaodong, SUN Hongbo, WU Le), CN=ArticleExt(id=1242136837523841297, articleId=1242136834256482550, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=图像美学质量评价技术发展趋势, columnId=1242116902361830275, journalTitle=科技导报, columnName=专题论文, runingTitle=null, highlight=null, articleAbstract=图像质量评价是利用计算机模拟人类视觉系统自动评价图像的失真程度,而图像美学质量评价是利用计算机模拟人类对美的感知与认知,自动评价图像的"美感",分析图像在构图、颜色、光影、景深、虚实等美学因素影响下所形成的美感刺激,这是计算美学与计算机视觉、心理学、虚拟现实等领域的学科交叉新方向,其在摄影摄像、电影电视、电子商务、服装设计、平面设计、文稿设计、工业设计、汽车行业、建筑行业、美容美妆等多个行业具有良好的应用前景。本文回顾了图像美学质量评价的发展历史,从基于人工设计美学特征的方法、基于美学特征深度学习的方法、图像美学质量评价的新任务、图像美学质量评价数据集构建等方面综述了该领域关键技术的发展情况,并展望了图像美学质量评价及其关键技术的发展趋势。, correspAuthors=null, authorNote=金鑫,讲师,研究方向为计算美学、虚拟现实、计算机视觉,电子信箱:jinxin@besti.edu.cn, correspAuthorsNote=周彬(通信作者),讲师,研究方向为虚拟现实、计算机图形学、机器人,电子信箱:zhoubin@buaa.edu.cn, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=WSFaDO7E1j+0eiFc3O0HcA==, pdfFileSize=3921633, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=1. 北京电子科技学院计算机科学与技术系, 北京 100070;
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科技导报
| 专题论文 2018, 36(9): 36-45
图像美学质量评价技术发展趋势
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金鑫1 , 周彬2 , 邹冬青3 , 李晓东1 , 孙红波1 , 吴乐1
作者信息
1. 北京电子科技学院计算机科学与技术系, 北京 100070;
2. 北京航空航天大学计算机学院, 北京 100191;
3. 北京市商汤科技开发有限公司, 北京 100084
通讯作者:
周彬(通信作者),讲师,研究方向为虚拟现实、计算机图形学、机器人,电子信箱:zhoubin@buaa.edu.cn
Image aesthetic quality assessment: A survey
Affiliations
出版时间: 2018-05-13
doi: 10.3981/j.issn.1000-7857.2018.09.005
文章导航
图像质量评价是利用计算机模拟人类视觉系统自动评价图像的失真程度,而图像美学质量评价是利用计算机模拟人类对美的感知与认知,自动评价图像的"美感",分析图像在构图、颜色、光影、景深、虚实等美学因素影响下所形成的美感刺激,这是计算美学与计算机视觉、心理学、虚拟现实等领域的学科交叉新方向,其在摄影摄像、电影电视、电子商务、服装设计、平面设计、文稿设计、工业设计、汽车行业、建筑行业、美容美妆等多个行业具有良好的应用前景。本文回顾了图像美学质量评价的发展历史,从基于人工设计美学特征的方法、基于美学特征深度学习的方法、图像美学质量评价的新任务、图像美学质量评价数据集构建等方面综述了该领域关键技术的发展情况,并展望了图像美学质量评价及其关键技术的发展趋势。
图像美学质量评价
/
计算美学
/
虚拟现实
/
计算艺术
/
视觉艺术
Image quality assessment (IQA) is to measure the perceived image degradation while image aesthetic quality assessment (IAQA) is to automatically give a review of aesthetics of an image. The composition, color, illumination, depth of field, blurness, sharpness, etc. are analyzed by means of computer to analog the aesthetic perception and cognition of human beings. IAQA is a novel interdisciplinary field of computer aesthetics and computer vision, psychology, and virtual reality. IAQA can be used for photography, movie and television, ecommerce, fashion design, graphic design, document typesetting, industry design, automobile industry, architectural design, beauty makeup, etc. In this article, we give a brief history of IAQA. Then we review the methods based on hand-crafted aesthetic feature design, the methods based on deep aesthetic feature learning, new topics in IAQA, and dataset collection for IAQA. Finally, we give the future directions of IAQA.
image aesthetic quality assessment
/
computational aesthetics
/
virtual reality
/
computational art
/
visual art
金鑫, 周彬, 邹冬青, 李晓东, 孙红波, 吴乐.
图像美学质量评价技术发展趋势.
科技导报,
2018
, 36
(9)
: 36
-45
.
DOI: 10.3981/j.issn.1000-7857.2018.09.005
JIN Xin, ZHOU Bin, ZOU Dongqing, LI Xiaodong, SUN Hongbo, WU Le.
Image aesthetic quality assessment: A survey[J].
Science & Technology Review ,
2018
, 36
(9)
: 36
-45
.
DOI: 10.3981/j.issn.1000-7857.2018.09.005
2018年第36卷第9期
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BibTeX
文章信息
doi: 10.3981/j.issn.1000-7857.2018.09.005
接收时间:2018-04-23
首发时间:2018-05-19
出版时间:2018-05-13
收稿日期:2018-04-23
修回日期:2018-05-03
通讯作者:
周彬(通信作者),讲师,研究方向为虚拟现实、计算机图形学、机器人,电子信箱:zhoubin@buaa.edu.cn
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2018.09.005
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2种不同金属材料的力学参数
科 Family 属数 Number of genus 种数 Number of species 占总种数比例 Percentage of total species (%) 属 Genus 种数 Number of species 占总种数比例 Percentage of total species (%) 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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