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Image Quality Assessment using Elman Neural Network Model and Interleaving Method
Paulraj M.P., Mohd Shuhanaz Zanar Azlan, Hema C R, Rajkumar Palaniappan
Pages - 51 - 57     |    Revised - 15-09-2012     |    Published - 25-10-2012
Volume - 3   Issue - 3    |    Publication Date - October 2012  Table of Contents
Neural Networks, Image Quuality Assesment, Vertical Interleaving, Feature Extraction
Imaging systems introduce distortions and artifacts to the image. It is crucial to know the quality of the image before processing. In any image processing application it is important to know reliability of the imaging system and the quality metrics of the image acquired using the imaging system. This research aims to develop, reference image quality measurement algorithms for JPEG images. A JPEG image database was created and subjective experiments were conducted on the database. A newly proposed image pixel reduction technique was applied to the image to reduce its size. An attempt to design a computationally inexpensive and memory efficient feature extraction method has been developed along with the interleaving method. Subjective test results are used to train the neural network model, which achieves good quality prediction performance without any reference image. In particular the Elman neural network model predicts the mean opinion score of the human observer. The system has been implemented and tested for its validity. Experimental results show that the proposed algorithms have an accuracy rate of 90.23% for image quality recognition.
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Dr. Paulraj M.P.
University Malaysia Perlis - Malaysia
Mr. Mohd Shuhanaz Zanar Azlan
University Malaysia Perlis - Malaysia
Dr. Hema C R
Karpagam University - India
Mr. Rajkumar Palaniappan
- Malaysia