Fuzzy Regression for Perceptual Image Quality Assessment
Subjective image quality assessment (IQA) is fundamentally important in various image processing applications such as image/video compression and image reconstruction, since it directly indicates the actual human perception of an image. However, fuzziness due to human judgment is neglected in curren...
| Main Authors: | , |
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| Format: | Journal Article |
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Elsevier B. V.
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/11495 |
| _version_ | 1848747821429161984 |
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| author | Chan, Kit Yan Engelke, U. |
| author_facet | Chan, Kit Yan Engelke, U. |
| author_sort | Chan, Kit Yan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Subjective image quality assessment (IQA) is fundamentally important in various image processing applications such as image/video compression and image reconstruction, since it directly indicates the actual human perception of an image. However, fuzziness due to human judgment is neglected in current methodologies for predicting subjective IQA, where the fuzziness indicates assessment uncertainty. In this article, we propose a fuzzy regression method that accounts for fuzziness introduced through human judgment and the limitations of widely-used psychometric quality scales. We demonstrate how fuzzy regression models provide fuzziness information regarding subjective IQA. We benchmark the fuzzy regression method against the commonly used explicit modeling method for subjective IQA namely statistical regression by considering three real situations involving subjective image quality experiments where: (a) the number of participants is insufficient; (b) an insufficient amount of data is used for modelling; and (c) variant fuzziness is caused by human judgment. Results indicate that fuzzy regression models achieve more effective data fitting and better generalization capability when predicting subjective IQA under different types and levels of image distortion. |
| first_indexed | 2025-11-14T06:55:14Z |
| format | Journal Article |
| id | curtin-20.500.11937-11495 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:55:14Z |
| publishDate | 2015 |
| publisher | Elsevier B. V. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-114952017-09-13T15:41:41Z Fuzzy Regression for Perceptual Image Quality Assessment Chan, Kit Yan Engelke, U. subjective image quality assessment mean opinion scores (MOS) Fuzzy regression objective image quality metric Subjective image quality assessment (IQA) is fundamentally important in various image processing applications such as image/video compression and image reconstruction, since it directly indicates the actual human perception of an image. However, fuzziness due to human judgment is neglected in current methodologies for predicting subjective IQA, where the fuzziness indicates assessment uncertainty. In this article, we propose a fuzzy regression method that accounts for fuzziness introduced through human judgment and the limitations of widely-used psychometric quality scales. We demonstrate how fuzzy regression models provide fuzziness information regarding subjective IQA. We benchmark the fuzzy regression method against the commonly used explicit modeling method for subjective IQA namely statistical regression by considering three real situations involving subjective image quality experiments where: (a) the number of participants is insufficient; (b) an insufficient amount of data is used for modelling; and (c) variant fuzziness is caused by human judgment. Results indicate that fuzzy regression models achieve more effective data fitting and better generalization capability when predicting subjective IQA under different types and levels of image distortion. 2015 Journal Article http://hdl.handle.net/20.500.11937/11495 10.1016/j.engappai.2015.04.007 Elsevier B. V. fulltext |
| spellingShingle | subjective image quality assessment mean opinion scores (MOS) Fuzzy regression objective image quality metric Chan, Kit Yan Engelke, U. Fuzzy Regression for Perceptual Image Quality Assessment |
| title | Fuzzy Regression for Perceptual Image Quality Assessment |
| title_full | Fuzzy Regression for Perceptual Image Quality Assessment |
| title_fullStr | Fuzzy Regression for Perceptual Image Quality Assessment |
| title_full_unstemmed | Fuzzy Regression for Perceptual Image Quality Assessment |
| title_short | Fuzzy Regression for Perceptual Image Quality Assessment |
| title_sort | fuzzy regression for perceptual image quality assessment |
| topic | subjective image quality assessment mean opinion scores (MOS) Fuzzy regression objective image quality metric |
| url | http://hdl.handle.net/20.500.11937/11495 |