NDLI: Image Quality Assessing by using NN and SVM

Content Provider	IEEE Xplore Digital Library
Author	Yu-Bing Tong Qing Chang Qi-Shan Zhang
Copyright Year	2006
Description	Author affiliation: Sch. of Electron. & Inf. Eng., Beihang Univ., Beijing (Yu-Bing Tong; Qing Chang; Qi-Shan Zhang)
Abstract	In the correlative curve of image subjective and objective quality assessing, there are some points that lower the performance of image quality assessing model. In this paper, the concept of isolated points was given and isolated points predicting was also illuminated. A new model was given based on NN-neural network and SVM-support vector machines with PSNR and SSIM-structure similarity, which were used as two indexes describing image quality. NN was used to obtain the mapping functions between objective quality assessing indexes and subjective quality assessing value. SVM was used to classify the images into different types. Then the images were accessed by using different mapping functions. The number of isolated points was reduced in the correlative curve of the new model. The results from simulation experiment showed the model was effective. The monotony of the model is 6.94% higher than PSNR and RMSE-root mean square error is 35.90% higher than PSNR
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	3987
Ending Page	3990
File Size	236409
Page Count	4
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.258796
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image quality Neural networks Support vector machines PSNR Image analysis Nonlinear distortion Distortion measurement Electronic mail Support vector machine classification Mean square error methods Neural network Image quality assessing
Content Type	Text
Resource Type	Article

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