The Effects of Depth of Field on Subjective Evaluation of Aesthetic Appeal and Image Quality of Photographs
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhang T. | - |
dc.contributor.author | Xie J. | - |
dc.contributor.author | Zhou X. | - |
dc.contributor.author | Choi C. | - |
dc.date.available | 2020-03-03T06:45:31Z | - |
dc.date.created | 2020-02-24 | - |
dc.date.issued | 2020-01 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/17742 | - |
dc.description.abstract | Aesthetic appeal and image quality are two important features of photographs, which play the dominant role when people clean their albums. Currently, the objective image quality assessment has been documented very well whereas the objective aesthetic appeal assessment algorithms are not developed well enough. This paper first subjectively evaluated image quality and aesthetic appeal separately of 339 photographs across different levels of depth of field. With the subjective data, the paper proposed two mathematical models to predict the subjective aesthetic appeal from subjective image quality. More specifically, depth of field, as a common photographic feature, was investigated to see how it influenced aesthetic appeal and image quality. 32 participants were asked to score for the aesthetic appeal and image quality. With these subjective scores, we used two methods-linear regression and deep neural networks-to build models separately to predict aesthetic appeal from image quality. We found that both models worked well on the valid dataset and the performance of the deep neural networks model was better than the linear regression model. © 2013 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.relation.isPartOf | IEEE Access | - |
dc.title | The Effects of Depth of Field on Subjective Evaluation of Aesthetic Appeal and Image Quality of Photographs | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000525410900026 | - |
dc.identifier.doi | 10.1109/ACCESS.2020.2966523 | - |
dc.identifier.bibliographicCitation | IEEE Access, v.8, pp.13467 - 13475 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85078706134 | - |
dc.citation.endPage | 13475 | - |
dc.citation.startPage | 13467 | - |
dc.citation.title | IEEE Access | - |
dc.citation.volume | 8 | - |
dc.contributor.affiliatedAuthor | Choi C. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Aesthetic appeal | - |
dc.subject.keywordAuthor | deep neural networks | - |
dc.subject.keywordAuthor | depth of field | - |
dc.subject.keywordAuthor | image quality | - |
dc.subject.keywordAuthor | linear regression | - |
dc.subject.keywordPlus | Deep neural networks | - |
dc.subject.keywordPlus | Linear regression | - |
dc.subject.keywordPlus | Neural networks | - |
dc.subject.keywordPlus | Photography | - |
dc.subject.keywordPlus | Quality control | - |
dc.subject.keywordPlus | Aesthetic appeals | - |
dc.subject.keywordPlus | Depth of field | - |
dc.subject.keywordPlus | Important features | - |
dc.subject.keywordPlus | Linear regression models | - |
dc.subject.keywordPlus | Neural networks model | - |
dc.subject.keywordPlus | Objective image quality assessment | - |
dc.subject.keywordPlus | Subjective evaluations | - |
dc.subject.keywordPlus | Subjective image quality | - |
dc.subject.keywordPlus | Image quality | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.