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In this work, the issues of time and compression efficiency in image
transformation and compression are addressed. The work is focused on the
Gabor image transformation, and two new methods for enhancing the efficacy
of the image transformation process have been presented. |
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The first method is an efficient Gabor-QR decomposition scheme for
computing the transform coefficients. The Gabor-QR decomposition is a
parallel matrix-based method that computes the exact coefficients without
a time-consuming iterative process as required by most other Gabor
Transform algorithms. Furthermore, the Gabor-QR decomposition subdivides
the whole transformation process into a one-time pre-processing step and
an efficient transformation step for computing the coefficients. The
proposed algorithm allows multiple images to be transformed efficiently,
bypassing the time-consuming pre-processing step. |
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In addition, the Maximum-Variance Basis Selection scheme for constructing
the incomplete basis sets for the transformation has been developed by
Papanikolopoulos' students. The proposed basis selection scheme exploits
the image statistics and the energy compacting characteristic of the
transformation process. The resulting incomplete Gabor transform has
greater time and compression efficiency than the complete transform. The
utilization of image statistics is particularly important in the
compression of images that have dominant high-frequency features and/or
peculiar statistical structures. Most transform-based compression systems
operate based on the assumption that the input images have mostly
low-frequency features. These compression systems will not perform
efficiently in some image domains in which the aforementioned assumption
does not hold. It has been shown that the proposed basis selection scheme
has the capability of capturing dominant image features across the entire
frequency plane and produces better results than common transform-based
compression techniques such as JPEG.
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