A Study on Image Quality Assessment using Neural Networks and Structure Similarty

Artificial Neural Network (ANN) is a method of the mathematical functions to simulate the human nervous cells in the processing system. The advantage of ANN is using the model of neural network system with constantly training to gain the accurate results. Structure Similarity (SSIM) comprise the image brightness, contrast, and structure,  it expresses the quality of the  images comprehensively. This research combines the Artificial Neural Network perceptrons and Structure Similarity characteristics to create different types of images suitable for weight value, expect through the video image intensifier to improve the visual identification, and provides the automatic image processing procedure in the future (e.g. analyze, detection, division, and identify). The “Image Intensifier Filter System” which could automaticaly strengthen the video image according to different types of video image is developed for this research.

[1]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[2]  J. Knott.  The organization of behavior: A neuropsychological theory , 1951 .

[3]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[4]  F. Attneave,et al.  The Organization of Behavior: A Neuropsychological Theory , 1949 .

[5]  Li-Chiu Chang,et al.  Real‐time recurrent learning neural network for stream‐flow forecasting , 2002 .

[6]  Zhou Wang,et al.  Translation insensitive image similarity in complex wavelet domain , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[7]  Zhou Wang,et al.  Video quality assessment based on structural distortion measurement , 2004, Signal Process. Image Commun..

[8]  Yen-Chang Chen,et al.  Estuary water-stage forecasting by using radial basis function neural network , 2003 .

[9]  Sanjit K. Mitra,et al.  Special issue on objective video quality metrics , 2004, Signal Process. Image Commun..

[10]  Zhou Wang,et al.  Spatial Pooling Strategies for Perceptual Image Quality Assessment , 2006, 2006 International Conference on Image Processing.

[11]  Zhou Wang,et al.  Multi-scale structural similarity for image quality assessment , 2003 .

[12]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[13]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[14]  Zhou Wang,et al.  Video quality assessment using a statistical model of human visual speed perception. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[15]  C.M. Kung,et al.  Fast Fractal Image Compression Base on Block Property , 2008, 2008 International Conference on Advanced Computer Theory and Engineering.