Performance analysis of two image vector quantization techniques

Author

Ahalt, Stanley C. ; Chen, Prakoon ; Krishnamurthy, Ashok K.

Author_Institution

Dept. of Electr. Eng., Ohio State Univ., Columbus, OH, USA

fYear

1989

fDate

0-0 1989

Firstpage

169

Abstract

The authors compare the Y. Linde, A. Buzo, and R.M. Gray vector quantization algorithm (1980) with the frequency-sensitive competitive learning neural network. Each of these techniques is applied to two images, and the distortion and SNR are measured for various size codebooks. The results show that the neural network technique of designing codebooks yields results that are very close to the optimal design. Multiple images are included to show the results of quantization for various bit rates.<>

Keywords

data compression; learning systems; neural nets; picture processing; SNR; data compression; distortion; frequency-sensitive competitive learning neural network; image compression; image vector quantization techniques; performance analysis; picture processing; size codebooks; Data compression; Image processing; Learning systems; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1989. IJCNN., International Joint Conference on

Conference_Location

Washington, DC, USA

Type

conf

DOI

10.1109/IJCNN.1989.118576

Filename

118576

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3279815