@inproceedings{045c7cdb8270457bbf7e5b129183d1e5,
title = "Adaptive vector quantizer for image compression using self-organization approach",
abstract = "A self-organization neural network architecture is used to implement the vector quantizer for image compression. A modified self-organization algorithm, which is based on the frequency upper-threshold and centroid learning rule, is utilized for constructing the codebooks. The performances of the self-organization network and the conventional algorithm for vector quantization are compared. This algorithm yields near-optimal results and is computationally efficient. The self-organization network approach is suitable for adaptive vector quantizers. The self-organization network approach uses massively parallel computing structures and is very promising for VLSI implementation.",
author = "Chen, {Oscal T.C.} and Sheu, {Bing J.} and Wai-Chi Fang",
year = "1992",
month = jan,
day = "1",
doi = "10.1109/ICASSP.1992.226039",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "385--388",
booktitle = "ICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing",
address = "United States",
note = "1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992 ; Conference date: 23-03-1992 Through 26-03-1992",
}