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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Institution:	Universiti Malaysia Perlis
Language:	English

id	my.unimap-20491
record_format	dspace
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

Image quality assessment using Elman neural network model

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