Measurement of visual quality is crucial
for various image and video processing applications. It is widely
applied in image acquisition, media transmission, video compression,
image/video restoration, etc.
The goal of image quality assessment (QA) is to develop a computable
quality metric which is able to properly evaluate image quality. The
primary criterion is better QA consistency with human judgment.
Computational complexity and resource limitations are also concerns
in a successful QA design. Many methods have been proposed up to
now. At the beginning, quality measurements were directly taken from
simple distance measurements, which refer to mathematically signal
fidelity, such as mean squared error or Minkowsky distance. Lately,
QA was extended to color space and the Fourier domain in which
images are better represented. Some existing methods also consider
the adaptive ability of human vision. Unfortunately, the Video
Quality Experts Group indicated that none of the more sophisticated
metrics showed any great advantage over other existing metrics.
This thesis proposes a general approach to the QA problem by
evaluating image information entropy. An information theoretic model
for the human visual system is proposed and an information theoretic
solution is presented to derive the proper settings. The quality
metric is validated by five subjective databases from different
research labs. The key points for a successful quality metric are
investigated. During the testing, our quality metric exhibits
excellent consistency with the human judgments and compatibility
with different databases. Other than full reference quality
assessment metric, blind quality assessment metrics are also
proposed. In order to predict quality without a reference image, two
concepts are introduced which quantitatively describe the
inter-scale dependency under a multi-resolution framework. Based on
the success of the full reference quality metric, several blind
quality metrics are proposed for five different types of distortions
in the subjective databases. Our blind metrics outperform all
existing blind metrics and also are able to deal with some
distortions which have not been investigated.
| Identifer | oai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/4916 |
| Date | January 2009 |
| Creators | Zhang, Di |
| Source Sets | University of Waterloo Electronic Theses Repository |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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