Predictive classified vector quantization

被引:21
|
作者
Ngan, King N. [1 ]
Koh, Hee C.
机构
[1] Monash Univ, Dept Elect & Syst Engn, Melbourne, Vic 3168, Australia
[2] Ngee Ann Polytech, Dept Elect Engn, Singapore 2159, Singapore
关键词
D O I
10.1109/83.148602
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A new vector quantization (VQ) scheme based on the classified vector quantization (CVQ) concept called the predictive classified vector quantization (PCVQ) is presented. Unlike the CVQ where the classification information has to be transmitted, PCVQ predicts it, thus saving valuable bit rate. Two classifiers, one operating in the Hadamard domain and the other in the spatial domain, were designed and tested. The prediction of classification information was performed in the spatial domain. The PCVQ schemes achieved bit rate reductions over the CVQ ranging from 20 to 32% for two commonly used color test images while maintaining the same acceptable image quality. Bit rates of 0.70-0.93 b per pixel (bpp) were obtained depending on the image and PCVQ scheme used.
引用
收藏
页码:269 / 280
页数:12
相关论文
共 50 条