甲蟲王

論文名稱	General-Tree-Structured Vector Quantizer for Image Progressive Coding Using the Smooth Side-Match Method
發表日期	2003-02-01
論文收錄分類	SCI
所有作者	Shiueng Bien Yang
作者順序	第一作者
通訊作者	否
刊物名稱	IEEE Transactions on Circuits and Systems for Video Technology
發表卷數	13
是否具有審稿制度	是
發表期數	2
期刊或學報出版地國別/地區	NATUSA-美國
發表年份	2003
發表月份	12
發表形式	電子期刊
所屬計劃案	無
可公開文檔
可公開文檔
可公開文檔
附件	General01186536.pdf

[英文摘要] :
Several tree-structured vector quantizers have
recently been proposed. However, owing to the fact that all
trees used are fixed M-ary tree-structured, the training samples
contained in each node must be artificially divided into a fixed
number of clusters. This paper presents a general-tree-structured
vector quantizer (GTSVQ) based on a genetic clustering
algorithm that can divide the training samples contained in each
node into more natural clusters. Also, the Huffman tree decoder
is used to achieve the optimal bit rate after the construction of
the general-tree-structured encoder. Progressive coding can be
accomplished by giving a series of distortion or rate thresholds.
Moreover, a smooth side-match method is presented herein
to enhance the performance of coding quality according to
the smoothness of the gray levels between neighboring blocks.
The combination of the Huffman tree decoder and the smooth
side-match method is proposed herein. Furthermore, the Lena
image can be coded by GTSVQ with 0.198 bpp and 34.3 dB in
peak signal-to-noise ratio.

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