NDLI: NR-IQA for noise-affected images using singular value decomposition

Content Provider	IET Digital Library
Author	Joshi, Piyush Prakash, Surya
Abstract	This study presents an efficient no-reference image quality assessment (NR-IQA) technique to assess the quality of images affected by noise. The proposed technique is based on two characteristics of the human eye (retina), namely the presence of centre-surround receptive field and visualisation utilising different spatial frequency channels. In the proposed technique, the authors model centre-surround receptive field using difference of Gaussians (DoG), whereas to mimic multiple frequencies in the centre-surround receptive field, they compute multiple DoG images of different values of standard deviations generated for different frequencies. Furthermore, the singular value decomposition-based features are obtained from the generated DoG images to estimate the image quality. The proposed technique does not require any training, neither based on distorted/original images nor based on subjective human scores, to assess the image quality. The performance of the proposed technique is being analysed on LIVE, TID08, CSIQ and SD-IVL databases and it shows that the proposed technique outperforms recently proposed NR and no-training/training-based IQA techniques. Experimental validation of the proposed technique in the big-data scenario of 10,000 noisy images also shows encouraging results.
Starting Page	183
Ending Page	191
Page Count	9
ISSN	17519675
Volume Number	13
e-ISSN	17519683
Issue Number	Issue 2, Apr (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2018.5160
Journal	IET Signal Processing
Publisher Date	2018-09-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Centre-surround Receptive Field Computer Vision And Image Processing Technique Difference of Gaussians DoG Image Feature Extraction Human Eye Image Denoising Image Resolution IQA Technique Learning in AI Linear Algebra No-reference Image Quality Assessment Technique Noise-affected Image Noisy Image NR-IQA Numerical Analysis Optical, Image And Video Signal Processing Singular Value Decomposition Spatial Frequency Channel Standard Deviations
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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