| Summary: | 碩士 === 國立海洋大學 === 航運技術研究所 === 86 === Image compression techniques are mainly relied on analyzing
the spatial or temporal correlation and exploiting the
redundancy of an image to achieve lower bit rates for
transmission and maintain the range of fidelity for the
reconstructed data. While spatial correlation is readily
exploited by various image compression techniques, relatively
little attention has been given to spectral correlation across
bands. In the thesis, we first introduce the KLT-JPEG method
which incorporates the best methods available to fully exploit
the spectral and spatial correlation in the data. KLT is
theoretically the optimum method to spectrally decorrelate the
data and standard DCT based JPEG image compression algorithm is
considered to be the most viable practical technique
available today. Then, we introduce the Fixed-Region KLT(FR-KLT)
method. By this method, the image is partitioned into sub-
regions with fixed size and KLT is applied to each sub-region.
The simulation results shows that the compression ratio of FR-
KLT is better than that of KLT-JPEG. Due to the region partition
of FR-KLT cannot be adjusted according to the spectral
characteristics of each sub-region, we propose a Variable-Region
KLT(VR-KLT) method. Using this method, we can combine the sub-
blocks with similar spectral characteristics into the same
region and we obtain better performance in compression ratio
than FR-KLT. However, VR-KLT could not achieve the purpose of
fast compressing image data by calculating the eigen
components of each sub-block at first. To overcome this problem,
we develop the Adaptive Variable-Region KLT(AVR-KLT) method
which incorporates an adaptive KLT algorithm and the VR-KLT
method. The AVR-KLT method has a better performance in
compression ratio and maintain the range of fidelity for the
reconstructed data. Moreover, the AVR-KLT could accelerate the
speed of compressing image data by implementing its parallel
architecture in hardware. In our studies, we also test the AVR-
KLT method for remote sensing satellite images, and the
simulation results show that we could get high compression ratio
for remote sensing satellite images.
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