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Image quality assessment using Elman neural network model

International Conference on Man Machine Systems (ICoMMS 2012) organized by School of Mechatronic Engineering, co-organized by The Institute of Engineer, Malaysia (IEM) and Society of Engineering Education Malaysia, 27th - 28th February 2012 at Bayview Beach Resort, Penang, Malaysia.

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Bibliographic Details
Main Authors: Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr., Palaniappan, Rajkumar, Mohd Shuhanaz, Zanar Azalan
Other Authors: paul@unimap.edu.my
Format: Working Paper
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2012
Subjects:
Image quality
Assessment
Neural network
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/20491
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spelling my.unimap-204912012-07-19T13:02:58Z Image quality assessment using Elman neural network model Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr. Palaniappan, Rajkumar Mohd Shuhanaz, Zanar Azalan paul@unimap.edu.my Image quality Assessment Neural network International Conference on Man Machine Systems (ICoMMS 2012) organized by School of Mechatronic Engineering, co-organized by The Institute of Engineer, Malaysia (IEM) and Society of Engineering Education Malaysia, 27th - 28th February 2012 at Bayview Beach Resort, Penang, Malaysia. Measurement of visual quality is of fundamental importance for numerous image and video processing applications, where the goal of quality assessment algorithms is to automatically assess the quality of images or videos in agreement with human quality judgments. This research aims to develop a no reference image quality measurement algorithms for JPEG images. A JPEG image database was created and subjective experiments were conducted on the database. An attempt to design a computationally inexpensive and memory efficient feature extraction method has been developed. Subjective test results are used to train the neural network model, which achieves good quality prediction performance without any reference image. The system has been implemented and tested for its validity. Experimental results show that the image quality was recognized correctly at a rate of 89.23%. 2012-07-19T13:02:58Z 2012-07-19T13:02:58Z 2012-02-27 Working Paper http://hdl.handle.net/123456789/20491 en Proceedings of the International Conference on Man-Machine Systems (ICoMMS 2012) Universiti Malaysia Perlis (UniMAP) School of Mechatronic Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Image quality
Assessment
Neural network
spellingShingle Image quality
Assessment
Neural network
Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.
Palaniappan, Rajkumar
Mohd Shuhanaz, Zanar Azalan
Image quality assessment using Elman neural network model
description International Conference on Man Machine Systems (ICoMMS 2012) organized by School of Mechatronic Engineering, co-organized by The Institute of Engineer, Malaysia (IEM) and Society of Engineering Education Malaysia, 27th - 28th February 2012 at Bayview Beach Resort, Penang, Malaysia.
author2 paul@unimap.edu.my
author_facet paul@unimap.edu.my
Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.
Palaniappan, Rajkumar
Mohd Shuhanaz, Zanar Azalan
format Working Paper
author Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.
Palaniappan, Rajkumar
Mohd Shuhanaz, Zanar Azalan
author_sort Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.
title Image quality assessment using Elman neural network model
title_short Image quality assessment using Elman neural network model
title_full Image quality assessment using Elman neural network model
title_fullStr Image quality assessment using Elman neural network model
title_full_unstemmed Image quality assessment using Elman neural network model
title_sort image quality assessment using elman neural network model
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/20491
_version_ 1643793088723812352
score 13.222552

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