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Published Articles >> Table of Contents >> Abstract
11th International Multimedia Modelling Conference (MMM'05)
pp. 247-254
Learning No-Reference Quality Metric by Examples
Hanghang Tong, Tsinghua University
Mingjing Li, Microsoft Research Asia
Hong-Jiang Zhang, Microsoft Research Asia
Changshui Zhang, Tsinghua University
Jingrui He, Tsinghua University
Wei-Ying Ma, Microsoft Research Asia
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MMMC.2005.52
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| Abstract |
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In this paper, a novel learning based method is
proposed for No-Reference image quality assessment.
Instead of examining the exact prior knowledge for the
given type of distortion and finding a suitable way to
represent it, our method aims to directly get the quality
metric by means of learning. At first, some training
examples are prepared for both high-quality and low-quality
classes; then a binary classifier is built on the
training set; finally the quality metric of an un-labeled
example is denoted by the extent to which it belongs to
these two classes. Different schemes to acquire
examples from a given image, to build the binary
classifier and to model the quality metric are proposed
and investigated. While most existing methods are
tailored for some specific distortion type, the proposed
method might provide a general solution for
No-Reference image quality assessment. Experimental
results on JPEG and JPEG2000 compressed images
validate the effectiveness of the proposed method.
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 Additional Information
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Citation:
Hanghang Tong, Mingjing Li, Hong-Jiang Zhang, Changshui Zhang, Jingrui He, Wei-Ying Ma,
"Learning No-Reference Quality Metric by Examples,"
mmm,
pp. 247-254,
11th International Multimedia Modelling Conference (MMM'05),
2005
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