Combined Blockiness, Blurriness and White Noise Distortion Meter

Combined Blockiness, Blurriness and White Noise Distortion Meter Due to discrete cosine transform (DCT) based compression, the loss appears in the form of blocky and blurry distortions in coded pictures. One of the degradation is white noise which corrupts nearly all pixels in the picture. In image transmission system, to assess the image quality at receiver end, lesser image features are sent, instead of the whole reference original picture to accommodate the available bandwidth. The purpose of this research is to propose a procedure which quantifies or estimates the objective picture quality automatically. The presented combined white noise image quality assessment meter (CWNIQAM) functions in the frequency domain for computation of three artefacts blockiness, blurriness and white noise in the corrupted coded images. The designed quality meter at first converts RGB pictures into YCbCr, gray scaled pictures and then edge detection method is applied. Afterward the picture is divided into blocks of 32 × 32 pixels to get local level blockiness, blurriness and white noise value. Next the picture is transformed from spatial to frequency domain and different features called reduced reference parameters are estimated. The composite magnitudes strength through horizontal and vertical harmonics is obtained for estimation of blocky and blurry artefacts in the coded images, while the strength of all ac coefficients and dc component is obtained for computation of white noise. The reduced reference features are computed and compared for reference and coded picture for estimating the particular type of distortion. The uniqueness of the proposed CWNIQAM meter is that, it can estimate the quality of corrupted images by estimating the combination of blockiness, blurriness and white noise through this single meter. The LIVE database2 having 174 different white noise (wn) corrupted images are used for measuring combined objective value of the three artefacts using the designed reduced reference image quality assessment meter algorithm. The objective results are correlated with subjective wn, DMOS LIVE scores. The correlation of about 98% is attained which indicate that much better picture quality index is achieved by the offered reduced reference combined white noise image quality evaluation meter in the frequency domain. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Wireless Personal Communications Springer Journals

Combined Blockiness, Blurriness and White Noise Distortion Meter

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Engineering; Communications Engineering, Networks; Signal,Image and Speech Processing; Computer Communication Networks
ISSN
0929-6212
eISSN
1572-834X
D.O.I.
10.1007/s11277-018-5888-x
Publisher site
See Article on Publisher Site

Abstract

Due to discrete cosine transform (DCT) based compression, the loss appears in the form of blocky and blurry distortions in coded pictures. One of the degradation is white noise which corrupts nearly all pixels in the picture. In image transmission system, to assess the image quality at receiver end, lesser image features are sent, instead of the whole reference original picture to accommodate the available bandwidth. The purpose of this research is to propose a procedure which quantifies or estimates the objective picture quality automatically. The presented combined white noise image quality assessment meter (CWNIQAM) functions in the frequency domain for computation of three artefacts blockiness, blurriness and white noise in the corrupted coded images. The designed quality meter at first converts RGB pictures into YCbCr, gray scaled pictures and then edge detection method is applied. Afterward the picture is divided into blocks of 32 × 32 pixels to get local level blockiness, blurriness and white noise value. Next the picture is transformed from spatial to frequency domain and different features called reduced reference parameters are estimated. The composite magnitudes strength through horizontal and vertical harmonics is obtained for estimation of blocky and blurry artefacts in the coded images, while the strength of all ac coefficients and dc component is obtained for computation of white noise. The reduced reference features are computed and compared for reference and coded picture for estimating the particular type of distortion. The uniqueness of the proposed CWNIQAM meter is that, it can estimate the quality of corrupted images by estimating the combination of blockiness, blurriness and white noise through this single meter. The LIVE database2 having 174 different white noise (wn) corrupted images are used for measuring combined objective value of the three artefacts using the designed reduced reference image quality assessment meter algorithm. The objective results are correlated with subjective wn, DMOS LIVE scores. The correlation of about 98% is attained which indicate that much better picture quality index is achieved by the offered reduced reference combined white noise image quality evaluation meter in the frequency domain.

Journal

Wireless Personal CommunicationsSpringer Journals

Published: Jun 5, 2018

References

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