Image and Video Component Quality Measurements, Analyses, and Applications.
Gao, Chen.
2015
-
Abstract: Currently, human experts provide the most reliable image quality
evaluations for biomedical, industrial, and security applications. However, subjective
evaluation by humans is impractical for real time applications. Therefore, it is desired to
create an evaluation process that can be automated and not involve human intervention. This
dissertation introduces a novel system for evaluat... read moreing image quality objectively. The system
utilizes multiple image measurements that evaluate one attribute aspect of an image. Each
of these attribute measures is inspired by human visual system properties, and can be used
separately as a standalone measure for evaluating a certain characteristic of an image.
Multiple attributes are then fused together to provide an overall comprehensive evaluation
for given image processing tasks. For the purpose of color image and video quality
evaluation, attributes such as color, sharpness, and contrast are considered, while in
applications where edge map qualities are important, edge pixel localization, corner
presence, and double edge occurrence are used instead. To examine the uses of the measures
for new image processing algorithm design and analysis, a set of alpha weighted quadratic
filter based methods are developed for both color image enhancement and edge detection
applications. The presented measures are used to automatically evaluate the image
processing algorithm performances, as well as to assist in the selection of optimal
operating parameters, all in accordance with the human visual system. The proposed measures
are also shown to be applicable as a new method for fast database searching and retrieval.
Furthermore, other new applications for the measures for monitoring thermal electrical
system conditions, evaluating food quality, inspecting and exploring underwater
environment, and for diagnosis and detecting of anomalies in biomedical images are
presented.
Thesis (Ph.D.)--Tufts University, 2015.
Submitted to the Dept. of Electrical Engineering.
Advisor: Karen Panetta.
Committee: Sos Agaian, Ronald Lasser, and Ethan Danahy.
Keyword: Electrical engineering.read less - ID:
- 5x21tt20z
- Component ID:
- tufts:21422
- To Cite:
- DCA Citation Guide EndNote
