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dc.contributor.authorCui, Xiaonanzh_CN
dc.contributor.authorShi, Zhiyuanzh_CN
dc.contributor.authorLin, Jiananzh_CN
dc.contributor.authorHuang, Lianfenzh_CN
dc.contributor.author施芝元zh_CN
dc.contributor.author黄联芬zh_CN
dc.date.accessioned2015-07-22T02:39:20Z
dc.date.available2015-07-22T02:39:20Z
dc.date.issued2012zh_CN
dc.identifier.citationINTERNATIONAL CONFERENCE ON SOLID STATE DEVICES AND MATERIALS SCIENCE, 2012,25:485-491zh_CN
dc.identifier.otherWOS:000305960300075zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/86489
dc.descriptionConference Name:International Conference on Solid State Devices and Materials Science (SSDMS). Conference Address: Macao, PEOPLES R CHINA. Time:APR 01-02, 2012.zh_CN
dc.description.abstractIn digital transmission, images may undergo quality degradation due to lossy compression and error-prone channels. Efficient measurement tools are needed to quantify induced distortions and to predict their impact on perceived quality. In this paper, an artifcial neural network (ANN) is proposed for perceptual image quality assessment. The quality prediction is based on image features such as EPSNR, blocking, and blur. Training and testing of the ANN are performed with the mean opinion scores (MOS) provided by the Laboratory for Image and Video Engineering (LIVE). It is shown that the proposed image quality assessment model is capable of predicting MOS of the five types' image distortions. (c) 2012 Published by Elsevier B.V. Selection and/or peer-review under responsibility of Garry Leezh_CN
dc.language.isoen_USzh_CN
dc.publisherPHYSCS PROCzh_CN
dc.source.urihttp://dx.doi.org/10.1016/j.phpro.2012.03.115zh_CN
dc.titleThe Research of Image Quality Assessment Methodszh_CN
dc.typeConferencezh_CN


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