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Proceedings Paper

Scene classification with respect to image quality measurements
Author(s): Kyung Hoon Oh; Sophie Triantaphillidou; Ralph E. Jacobson
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Paper Abstract

Psychophysical image quality assessments have shown that subjective quality depended upon the pictorial content of the test images. This study is concerned with the nature of scene dependency, which causes problems in modeling and predicting image quality. This paper focuses on scene classification to resolve this issue and used K-means clustering to classify test scenes. The aim was to classify thirty two original test scenes that were previously used in a psychophysical investigation conducted by the authors, according to their susceptibility to sharpness and noisiness. The objective scene classification involved: 1) investigation of various scene descriptors, derived to describe properties that influence image quality, and 2) investigation of the degree of correlation between scene descriptors and scene susceptibility parameters. Scene descriptors that correlated with scene susceptibility in sharpness and in noisiness are assumed to be useful in the objective scene classification. The work successfully derived three groups of scenes. The findings indicate that there is a potential for tackling the problem of sharpness and noisiness scene susceptibility when modeling image quality. In addition, more extensive investigations of scene descriptors would be required at global and local image levels in order to achieve sufficient accuracy of objective scene classification.

Paper Details

Date Published: 18 January 2010
PDF: 10 pages
Proc. SPIE 7529, Image Quality and System Performance VII, 752908 (18 January 2010); doi: 10.1117/12.838302
Show Author Affiliations
Kyung Hoon Oh, Univ. of Westminster (United Kingdom)
Sophie Triantaphillidou, Univ. of Westminster (United Kingdom)
Ralph E. Jacobson, Univ. of Westminster (United Kingdom)

Published in SPIE Proceedings Vol. 7529:
Image Quality and System Performance VII
Susan P. Farnand; Frans Gaykema, Editor(s)

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