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| Field |
Value |
| Title: | A sharpness measure for image quality assessment using average effective number of neighbors |
| Authors: | Liao, Wen-Hung
廖文宏 |
| Contributors: | 資科系 |
| Keywords: | Artificial intelligence; Effective approaches; effective number of neighbors; Image quality assessment; Image quality metrics; Imaging device; No-reference images; Quality metrices; Sharpness measures; Image quality |
| Date: | 2013-12 |
| Issue Date: | 2015-05-26 18:28:26 (UTC+8) |
| Abstract: | The proliferation of portable and miniaturized imaging devices, coupled with the prevalence of communication networks have changed the way we create and share photos. Indices for image quality have been proposed extensively to evaluate the recorded photograph. In this paper, we first delineate the desirable properties of an image quality metric. We then describe a computationally effective approach to assess the sharpness of a photo so that images of poor focus can be identified. The proposed method attempts to measure the integrity of major structures by computing the effective number of neighbors (ENN) for strong edge pixels in an image. Simulations and experimental results indicate that this ENN-based metric conforms to all the desired properties of a quality metric and is able to estimate the blurredness effectively and efficiently. © 2013 IEEE. |
| Relation: | Proceedings - 2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013, 2013, 論文編號 6783859, 152-157, 2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013; Taipei; Taiwan; 6 December 2013 到 8 December 2013; 類別編號E2528; 代碼 104746 |
| Data Type: | conference |
| DOI: | http://dx.doi.org/10.1109/TAAI.2013.40 |
| DCField |
Value |
Language |
| dc.contributor (Contributor) | 資科系 | |
| dc.creator (Authors) | Liao, Wen-Hung | |
| dc.creator (Authors) | 廖文宏 | zh_TW |
| dc.date (Date) | 2013-12 | |
| dc.date.accessioned | 2015-05-26 18:28:26 (UTC+8) | - |
| dc.date.available | 2015-05-26 18:28:26 (UTC+8) | - |
| dc.date.issued (Issue Date) | 2015-05-26 18:28:26 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/75330 | - |
| dc.description.abstract (Abstract) | The proliferation of portable and miniaturized imaging devices, coupled with the prevalence of communication networks have changed the way we create and share photos. Indices for image quality have been proposed extensively to evaluate the recorded photograph. In this paper, we first delineate the desirable properties of an image quality metric. We then describe a computationally effective approach to assess the sharpness of a photo so that images of poor focus can be identified. The proposed method attempts to measure the integrity of major structures by computing the effective number of neighbors (ENN) for strong edge pixels in an image. Simulations and experimental results indicate that this ENN-based metric conforms to all the desired properties of a quality metric and is able to estimate the blurredness effectively and efficiently. © 2013 IEEE. | |
| dc.format.extent | 176 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (Relation) | Proceedings - 2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013, 2013, 論文編號 6783859, 152-157, 2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013; Taipei; Taiwan; 6 December 2013 到 8 December 2013; 類別編號E2528; 代碼 104746 | |
| dc.subject (Keywords) | Artificial intelligence; Effective approaches; effective number of neighbors; Image quality assessment; Image quality metrics; Imaging device; No-reference images; Quality metrices; Sharpness measures; Image quality | |
| dc.title (Title) | A sharpness measure for image quality assessment using average effective number of neighbors | |
| dc.type (Data Type) | conference | en |
| dc.identifier.doi (DOI) | 10.1109/TAAI.2013.40 | |
| dc.doi.uri | http://dx.doi.org/10.1109/TAAI.2013.40 | |
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