Digital Image Quality Prediction System
“A picture is worth a thousand words.” Based on this well-known adage, we can say that images are important in our society, and increasingly so. Currently, the Internet is the main channel of socialization and marketing, where we seek to communicate in the most efficient way possible. People receive...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-3900/54/1/15 |
| _version_ | 1827091104195936256 |
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| author | Nereida Rodriguez-Fernandez Iria Santos Alvaro Torrente-Patiño Adrian Carballal |
| author_facet | Nereida Rodriguez-Fernandez Iria Santos Alvaro Torrente-Patiño Adrian Carballal |
| author_sort | Nereida Rodriguez-Fernandez |
| collection | DOAJ |
| description | “A picture is worth a thousand words.” Based on this well-known adage, we can say that images are important in our society, and increasingly so. Currently, the Internet is the main channel of socialization and marketing, where we seek to communicate in the most efficient way possible. People receive a large amount of information daily and that is where the need to attract attention with quality content and good presentation arises. Social networks, for example, are becoming more visual every day. Only on Facebook can you see that the success of a publication increases up to 180% if it is accompanied by an image. That is why it is not surprising that platforms such as Pinterest and Instagram have grown so much, and have positioned themselves thanks to their power to communicate with images. In a world where more and more relationships and transactions are made through computer applications, many decisions are made based on the quality, aesthetic value or impact of digital images. In the present work, a quality prediction system for digital images was developed, trained from the quality perception of a group of humans. |
| first_indexed | 2024-03-10T17:12:53Z |
| format | Article |
| id | doaj.art-1bfc6e41fbaa4548a66e20d003d06517 |
| institution | Directory Open Access Journal |
| issn | 2504-3900 |
| language | English |
| last_indexed | 2025-03-20T05:50:59Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Proceedings |
| spelling | doaj.art-1bfc6e41fbaa4548a66e20d003d065172024-10-03T02:26:09ZengMDPI AGProceedings2504-39002020-08-015411510.3390/proceedings2020054015Digital Image Quality Prediction SystemNereida Rodriguez-Fernandez0Iria Santos1Alvaro Torrente-Patiño2Adrian Carballal3CITIC-Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña, SpainCITIC-Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña, SpainDepartment of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, Campus Elviña s/n, 15071 A Coruña, SpainCITIC-Research Center of Information and Communication Technologies, University of A Coruña, 15071 A Coruña, Spain“A picture is worth a thousand words.” Based on this well-known adage, we can say that images are important in our society, and increasingly so. Currently, the Internet is the main channel of socialization and marketing, where we seek to communicate in the most efficient way possible. People receive a large amount of information daily and that is where the need to attract attention with quality content and good presentation arises. Social networks, for example, are becoming more visual every day. Only on Facebook can you see that the success of a publication increases up to 180% if it is accompanied by an image. That is why it is not surprising that platforms such as Pinterest and Instagram have grown so much, and have positioned themselves thanks to their power to communicate with images. In a world where more and more relationships and transactions are made through computer applications, many decisions are made based on the quality, aesthetic value or impact of digital images. In the present work, a quality prediction system for digital images was developed, trained from the quality perception of a group of humans.https://www.mdpi.com/2504-3900/54/1/15machine learninggenetic algorithmqualityimagepredictiondataset |
| spellingShingle | Nereida Rodriguez-Fernandez Iria Santos Alvaro Torrente-Patiño Adrian Carballal Digital Image Quality Prediction System Proceedings machine learning genetic algorithm quality image prediction dataset |
| title | Digital Image Quality Prediction System |
| title_full | Digital Image Quality Prediction System |
| title_fullStr | Digital Image Quality Prediction System |
| title_full_unstemmed | Digital Image Quality Prediction System |
| title_short | Digital Image Quality Prediction System |
| title_sort | digital image quality prediction system |
| topic | machine learning genetic algorithm quality image prediction dataset |
| url | https://www.mdpi.com/2504-3900/54/1/15 |
| work_keys_str_mv | AT nereidarodriguezfernandez digitalimagequalitypredictionsystem AT iriasantos digitalimagequalitypredictionsystem AT alvarotorrentepatino digitalimagequalitypredictionsystem AT adriancarballal digitalimagequalitypredictionsystem |