| Summary: | 碩士 === 國立中正大學 === 電機工程研究所 === 82 === Vector Quantization (VQ) is a powerful and effective sc- heme
that is widely used in speech and image coding applicat ions.
One basic problem associated with VQ is its sensitivity to
channel errors. In this thesis, the performance of a low-
complexity VQ----the Tree-Structured VQ (TSVQ) when used over
noisy channels is first analyzed. Next, we study a class of VQ
with memory that is known as Side-Match VQ (SMVQ) in the prese
nce of channel noise. Especially, when channel noise is present
, the ordinary SMVQ performance degrades drastically. We inve
stigate, respectively, the modified algorithm that is taking
the channel noise into account for TSVQ and SMVQ. Extensive sim
ulation results are given for the image source. Comparsion the
ordinary TSVQ and SMVQ designed for the noiseless channel show
substantial improvements when the channel is noisy. 4 dB gain
is approximately obtained for our TSVQ design. ALSO, 4.7 dB
improvement is achieved by the proposed SMVQ decoding algorithm.
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