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題名 A sharpness measure for image quality assessment using average effective number of neighbors
作者 Liao, Wen-Hung
廖文宏
貢獻者 資科系
關鍵詞 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
日期 2013-12
上傳時間 26-May-2015 18:28:26 (UTC+8)
摘要 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.
關聯 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
資料類型 conference
DOI http://dx.doi.org/10.1109/TAAI.2013.40
dc.contributor 資科系
dc.creator (作者) Liao, Wen-Hung
dc.creator (作者) 廖文宏zh_TW
dc.date (日期) 2013-12
dc.date.accessioned 26-May-2015 18:28:26 (UTC+8)-
dc.date.available 26-May-2015 18:28:26 (UTC+8)-
dc.date.issued (上傳時間) 26-May-2015 18:28:26 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/75330-
dc.description.abstract (摘要) 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 (關聯) 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 (關鍵詞) 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 (題名) A sharpness measure for image quality assessment using average effective number of neighbors
dc.type (資料類型) conferenceen
dc.identifier.doi (DOI) 10.1109/TAAI.2013.40
dc.doi.uri (DOI) http://dx.doi.org/10.1109/TAAI.2013.40