An analysis of image quality assessment algorithm to detect the presence of unnatural contrast enhancement
Image contrast enhancement purposely aim the visibility of image to be increased. Most of these problems may happen after contrast enhancement: amplification of noise artifacts, saturation-loss of details, excessive brightness change and unnatural contrast enhancement. The main objective of this pap...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Asian Research Publication Network
2016
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| Online Access: | http://psasir.upm.edu.my/id/eprint/16560/1/An%20analysis%20of%20image%20quality%20assessment%20algorithm%20to%20detect%20the%20presence%20of%20unnatural%20contrast%20enhancement.pdf |
| _version_ | 1825945839024996352 |
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| author | Ismail, Nur Halilah Chen, Soong Der Ng, Liang Shing Ramli, Abd Rahman |
| author_facet | Ismail, Nur Halilah Chen, Soong Der Ng, Liang Shing Ramli, Abd Rahman |
| author_sort | Ismail, Nur Halilah |
| collection | UPM |
| description | Image contrast enhancement purposely aim the visibility of image to be increased. Most of these problems may happen after contrast enhancement: amplification of noise artifacts, saturation-loss of details, excessive brightness change and unnatural contrast enhancement. The main objective of this paper is to present an extensive review on existing Image Quality Assessment Algorithm (IQA) in order to detect the presence of unnatural contrast enhancement. Basically, the IQA used produced quality rating of the image while consistently with human visual perception. Current IQA to detect presence of unnatural contrast enhancement: Lightness Order Error (LOE), Structure Measure Operator (SMO) and Statistical Naturalness Measure (SNM). However, result of current IQA evaluation shows it may not giving consistent quality rating with human visual perception. Among three IQAs, SNM demonstrate better performance compared to LOE and SMO. But, it suffers with consistent rating for different spatial image resolution in same image content. Thus, an improvement suggested in this paper to overcome such problem occurred. |
| first_indexed | 2024-03-06T07:37:46Z |
| format | Article |
| id | upm.eprints-16560 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T07:37:46Z |
| publishDate | 2016 |
| publisher | Asian Research Publication Network |
| record_format | dspace |
| spelling | upm.eprints-165602016-06-08T01:54:56Z http://psasir.upm.edu.my/id/eprint/16560/ An analysis of image quality assessment algorithm to detect the presence of unnatural contrast enhancement Ismail, Nur Halilah Chen, Soong Der Ng, Liang Shing Ramli, Abd Rahman Image contrast enhancement purposely aim the visibility of image to be increased. Most of these problems may happen after contrast enhancement: amplification of noise artifacts, saturation-loss of details, excessive brightness change and unnatural contrast enhancement. The main objective of this paper is to present an extensive review on existing Image Quality Assessment Algorithm (IQA) in order to detect the presence of unnatural contrast enhancement. Basically, the IQA used produced quality rating of the image while consistently with human visual perception. Current IQA to detect presence of unnatural contrast enhancement: Lightness Order Error (LOE), Structure Measure Operator (SMO) and Statistical Naturalness Measure (SNM). However, result of current IQA evaluation shows it may not giving consistent quality rating with human visual perception. Among three IQAs, SNM demonstrate better performance compared to LOE and SMO. But, it suffers with consistent rating for different spatial image resolution in same image content. Thus, an improvement suggested in this paper to overcome such problem occurred. Asian Research Publication Network 2016 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/16560/1/An%20analysis%20of%20image%20quality%20assessment%20algorithm%20to%20detect%20the%20presence%20of%20unnatural%20contrast%20enhancement.pdf Ismail, Nur Halilah and Chen, Soong Der and Ng, Liang Shing and Ramli, Abd Rahman (2016) An analysis of image quality assessment algorithm to detect the presence of unnatural contrast enhancement. Journal of Theoretical and Applied Information Technology, 83 (3). pp. 415-422. ISSN 1992-8645; ESSN: 1817-3195 http://www.jatit.org/volumes/eightythree3.php |
| spellingShingle | Ismail, Nur Halilah Chen, Soong Der Ng, Liang Shing Ramli, Abd Rahman An analysis of image quality assessment algorithm to detect the presence of unnatural contrast enhancement |
| title | An analysis of image quality assessment algorithm to detect the presence of unnatural contrast enhancement |
| title_full | An analysis of image quality assessment algorithm to detect the presence of unnatural contrast enhancement |
| title_fullStr | An analysis of image quality assessment algorithm to detect the presence of unnatural contrast enhancement |
| title_full_unstemmed | An analysis of image quality assessment algorithm to detect the presence of unnatural contrast enhancement |
| title_short | An analysis of image quality assessment algorithm to detect the presence of unnatural contrast enhancement |
| title_sort | analysis of image quality assessment algorithm to detect the presence of unnatural contrast enhancement |
| url | http://psasir.upm.edu.my/id/eprint/16560/1/An%20analysis%20of%20image%20quality%20assessment%20algorithm%20to%20detect%20the%20presence%20of%20unnatural%20contrast%20enhancement.pdf |
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