An adaptive vector quantization based on neural network

Author

Bensheng, Qiu ; Jianqin, Qi ; AnPin ; Diancheng, Zhang

Author_Institution

AI Inst., Hefei Univ. of Technol., China

Volume

2

fYear

1996

Firstpage

1413

Abstract

Some vector quantization algorithm are first surveyed. Then, an adaptive vector quantization method for image coding based on a neural network is proposed. This method first partitions the image into a subimage and transforms them with the DCT, and then classifies and encodes them in the transformed domain using frequency sensitive competitive learning (FSCL). The experimental results show that this VQ method has no local region distortion and a high compression ratio

Keywords

adaptive signal processing; discrete cosine transforms; image coding; image segmentation; neural nets; transform coding; vector quantisation; DCT; VQ method; adaptive vector quantization; experimental results; frequency sensitive competitive learning; high compression ratio; image classification; image coding; image partitioning; neural network; subimage; transform coding; transformed domain; vector quantization algorithm; Algorithm design and analysis; Books; Discrete cosine transforms; Image coding; Iterative algorithms; Neural networks; Power capacitors; Signal design; Signal processing; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing, 1996., 3rd International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-2912-0

Type

conf

DOI

10.1109/ICSIGP.1996.566588

Filename

566588