Volume & Issue no: Volume 5, Issue 2, March - April 2016
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Title: |
A Robust Method For Completely Blind Image
Quality Evaluator With Enriched Features
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Author Name: |
Prof. Sushilkumar N. Holambe, Priyanka M. Kshirsagar |
Abstract: |
Abstract
An important aim of study on the blind image quality
assessment (IQA) problem is to devise perceptual model that
can predict the quality of distorted images with prior
knowledge of the images or their distortions. The existing no
reference (NR) IQA algorithms require some knowledge
about distortions in the form of training examples and
corresponding human opinion scores. This type of quality
assessment methods are called as Opinion Aware methods.
However we have recently derived a blind IQA model which is
Opinion Unaware that does not require human subjective
scores for training. Thus, it is ‘completely blind’. The new
IQA model, which we call the Natural Image Quality
Evaluator (NIQE) is based on the construction of a collection
of ‘quality aware’ statistical features based on domain
natural scene statistic (NSS) model. We learn a multivariate
Gaussian model of image patches by integrating the features
of natural image statistics. Using this learned multivariate
Gaussian model, a Bhattacharyya-like distance is used to
measure the quality of each image patch, and then an overall
quality score of the image is obtained by average pooling the
qualities of each image patch. Thus opinion-unaware BIQA
method has shown consistently better quality prediction
accuracy than the opinion-aware method.
Keywords: completely blind, no reference, image quality
assessment, multivariate Gaussian |
Cite this article: |
Prof. Sushilkumar N. Holambe, Priyanka M. Kshirsagar , "
A Robust Method For Completely Blind Image
Quality Evaluator With Enriched Features
" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 5, Issue 2, March - April 2016 , pp.
213-217 , ISSN 2278-6856.
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