DocumentCode
3410331
Title
A high performance adaptive image compression system using a generative neural network: DynAmic Neural Network II (DANN II)
Author
Rios, Andres ; Kabuka, Mansur R.
Author_Institution
Dept. of Electr. & Comput. Eng., Miami Univ., Coral Gables, FL, USA
fYear
1993
fDate
1993
Firstpage
204
Lastpage
213
Abstract
The system is guaranteed theoretically to compress to any feasible rate, with as low a distortion rate as required. It also exhibits user selectable compression and error rates, ability to compress general data types, and adaptation to the data source. The compression system is based on a novel family of connectionist algorithms and generative algorithms used in conjunction with new artificial neural network models that permit the determination of a quasi-optimal architecture for compressing a given data source
Keywords
adaptive systems; data compression; image processing; neural nets; DANN II; DynAmic Neural Network II; adaptive image compression system; artificial neural network models; connectionist algorithms; generative algorithms; generative neural network; quasi-optimal architecture; Adaptive systems; Artificial neural networks; Computer architecture; Data compression; Error analysis; Image coding; Neural networks; Neurons; Programmable control; Rate distortion theory;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Compression Conference, 1993. DCC '93.
Conference_Location
Snowbird, UT
Print_ISBN
0-8186-3392-1
Type
conf
DOI
10.1109/DCC.1993.253129
Filename
253129
Link To Document