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Chang-Shyan Lin

dblp:118/6829 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1996
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Image and video coding · 100%
Theoretical computer science
1 paper
Coding theory · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video coding › quantization › vector quantization
side-match vector quantization
0.011996
Viterbi-based algorithm for side-match vector quantization over noisy channels · IEEE Trans. Commun. 1996
Image and video coding › quantization
vector quantization
0.011996
Viterbi-based algorithm for side-match vector quantization over noisy channels · IEEE Trans. Commun. 1996
Coding theory › error-correcting codes › convolutional codes › error propagation
catastrophic encoders
0.011996
Viterbi-based algorithm for side-match vector quantization over noisy channels · IEEE Trans. Commun. 1996
Coding theory › error-correcting codes › convolutional codes
error propagation
0.011996
Viterbi-based algorithm for side-match vector quantization over noisy channels · IEEE Trans. Commun. 1996

Methods — techniques the papers use, named apart from their topics

viterbi algorithm · 0.0noise detection · 0.0
YearPublicationVenuePosition
1996 Viterbi-based algorithm for side-match vector quantization over noisy channels
abstract
Side-match vector quantization is a finite-state technique for image coding. This research shows that the side-match vector quantization is an error-propagating code and it is similar to a catastrophic convolutional code. Here, we propose a Viterbi-based algorithm to solve this problem. Various noise detection algorithms are integrated into the Viterbi algorithm (to yield the Viterbi-based algorithm) for a much better performance. According to the simulation of a binary symmetric channel with random bit-error rate (BER) 0.1%-0.01%, the Viterbi-based algorithm provides 2.8-9.6 dB and 2.3-8.4 dB gain compared with the conventional side-match vector quantization decoder and the Viterbi decoder, respectively. In addition, the proposed algorithm requires much fewer computations than the Viterbi algorithm.
Chung J. Kuo, Chang-Shyan Lin
IEEE Trans. Commun.2