DCT statistics model-based blind image quality assessment

We propose an efficient, general-purpose, distortion-agnostic, blind/no-reference image quality assessment (NR-IQA) algorithm based on a natural scene statistics model of discrete cosine transform (DCT) coefficients. The algorithm is computationally appealing, given the availability of platforms opt...

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Bibliographic Details
Published in2011 18th IEEE International Conference on Image Processing pp. 3093 - 3096
Main Authors Saad, M. A., Bovik, A. C., Charrier, C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2011
Subjects
Computational modeling
discrete cosine transform
Discrete cosine transforms
Feature extraction
generalized Gaussian density
Image quality
natural scene statistics
No-reference image quality assessment
Predictive models
Probabilistic logic
Training
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ISBN1457713047
9781457713040
ISSN1522-4880
DOI10.1109/ICIP.2011.6116319

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Summary:We propose an efficient, general-purpose, distortion-agnostic, blind/no-reference image quality assessment (NR-IQA) algorithm based on a natural scene statistics model of discrete cosine transform (DCT) coefficients. The algorithm is computationally appealing, given the availability of platforms optimized for DCT computation. We propose a generalized parametric model of the extracted DCT coefficients. The parameters of the model are utilized to predict image quality scores. The resulting algorithm, which we name BLIINDS-II, requires minimal training and adopts a simple probabilistic model for score prediction. When tested on the LIVE IQA database, BLIINDS-II is shown to correlate highly with human visual perception of quality, at a level that is even competitive with the powerful full-reference SSIM index.
ISBN:1457713047
9781457713040
ISSN:1522-4880
DOI:10.1109/ICIP.2011.6116319