TY - GEN
T1 - Learning no-reference quality metric by examples
AU - Tong, Hanghang
AU - Li, Mingjing
AU - Zhang, Hong-Jiang
AU - Zhang, Changshui
AU - He, Jingrui
AU - Ma, Wei-Ying
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2005
Y1 - 2005
N2 - In this paper, a novel learning based method is proposed for No-Reference image quality assessment. Instead of examining the exact prior knowledge for the given type of distortion and finding a suitable way to represent it, our method aims to directly get the quality metric by means of learning. At first, some training examples are prepared for both high-quality and low-quality classes; then a binary classifier is built on the training set; finally the quality metric of an un-labeled example is denoted by the extent to which it belongs to these two classes. Different schemes to acquire examples from a given image, to build the binary classifier and to model the quality metric are proposed and investigated. While most existing methods are tailored for some specific distortion type, the proposed method might provide a general solution for No-Reference image quality assessment. Experimental results on JPEG and JPEG2000 compressed images validate the effectiveness of the proposed method. © 2005 IEEE.
AB - In this paper, a novel learning based method is proposed for No-Reference image quality assessment. Instead of examining the exact prior knowledge for the given type of distortion and finding a suitable way to represent it, our method aims to directly get the quality metric by means of learning. At first, some training examples are prepared for both high-quality and low-quality classes; then a binary classifier is built on the training set; finally the quality metric of an un-labeled example is denoted by the extent to which it belongs to these two classes. Different schemes to acquire examples from a given image, to build the binary classifier and to model the quality metric are proposed and investigated. While most existing methods are tailored for some specific distortion type, the proposed method might provide a general solution for No-Reference image quality assessment. Experimental results on JPEG and JPEG2000 compressed images validate the effectiveness of the proposed method. © 2005 IEEE.
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84891541368&origin=recordpage
U2 - 10.1109/MMMC.2005.52
DO - 10.1109/MMMC.2005.52
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0769521649
SN - 9780769521640
T3 - Proceedings of the 11th International Multimedia Modelling Conference, MMM 2005
SP - 247
EP - 254
BT - Proceedings of the 11th International Multimedia Modelling Conference, MMM 2005
T2 - 11th International Multimedia Modelling Conference, MMM 2005
Y2 - 12 January 2005 through 14 January 2005
ER -