The goal of quality assessment (QA) research is
to design algorithms that can automatically assess the quality
of images in a perceptually consistent manner. Image QA
algorithms generally interpret image quality as fidelity or
similarity with a “reference” or “perfect” image in some
perceptual space. In order to improve the assessment accuracy
of white noise, Gauss blur, JPEG2000 compression and other
distorted images, this paper puts forward an image quality
assessment method based on phase congruency and gradient
magnitude. The experimental results show that the image
quality assessment method has a higher accuracy than
traditional method and it can accurately reflect the image
visual perception of the human eye. In this paper, we propose
an image information measure that quantifies the information
that is present in the reference image and how much of this
reference information can be extracted from the distorted
image.
Published In : IJCAT Journal Volume 1, Issue 11
Date of Publication : 31 December 2014
Pages : 582 - 586
Figures :03
Tables : 01
Publication Link :Image Quality Assessment Technique Using
Gradient Magnitude Similarity with Phase
Congruency
Rohit Kumar : Department of Electronics & Communication
Vishal Moyal : Department of Electronics & Communication
Image quality assessment (IQA)
Structural
similarity index (SSIM)
Phase congruency (PC)/p>
Gradient
magnitude (GM)
Low level feature
In this paper, we proposed a novel efficient and
effective IQA index, based on a specific visual saliency
model.This is designed based on the assumption that an
image’s visual saliency map has a close relationship
with its perceptual quality. Experimental results indicate
that it could yield statistically better prediction
performance than all the other competing methods
evaluated. Thus,it can be the best candidate of IQA
indices for real time applications.
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