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JACIII Vol.22 No.5 pp. 725-730
doi: 10.20965/jaciii.2018.p0725
(2018)

Paper:

Blind Image Quality Assessment Based on Natural Statistics of Double-Opponency

Edwin Sybingco and Elmer P. Dadios

De La Salle University
2401 Taft Avenue, Manila 0922, Philippines

Received:
March 26, 2018
Accepted:
June 18, 2018
Published:
September 20, 2018
Keywords:
double-opponency, generalized Gaussian distribution, asymmetric generalized Gaussian distribution, feedforward neural network
Abstract

One of the challenges in image quality assessment (IQA) is to determine the quality score without the presence of the reference image. In this paper, the authors proposed a no-reference image quality assessment method based on the natural statistics of double-opponent (DO) cells. It utilizes the statistical modeling of the three opponency channels using the generalized Gaussian distribution (GGD) and asymmetric generalized Gaussian distribution (AGGD). The parameters of GGD and AGGD are then applied to feedforward neural network to predict the image quality. Result shows that for any opposing channels, its natural statistics parameters when applied to feedforward neural network can achieve satisfactory prediction of image quality.

Cite this article as:
E. Sybingco and E. Dadios, “Blind Image Quality Assessment Based on Natural Statistics of Double-Opponency,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.5, pp. 725-730, 2018.
Data files:
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Last updated on Dec. 13, 2018