DocumentCode
328873
Title
Edge preserving vector quantization using self-organizing map based on adaptive learning
Author
Kim, K.Y. ; Ra, J.B.
Author_Institution
Dept. of Electr. Eng., Korea Adv. Inst. of Sci. & Technol., Seoul, South Korea
Volume
2
fYear
1993
fDate
25-29 Oct. 1993
Firstpage
1219
Abstract
The conventional self-organizing map algorithm for vector quantization is modified to reduce the edge degradation in the reproduced image. The learning procedure is performed by a proper selection of the learning rate, which is adaptively determined according to the block activity. The simulation results of 4×4 vector quantization for 512×512 image coding show the feasibility of the proposed method.
Keywords
adaptive signal processing; edge detection; image coding; image reconstruction; learning (artificial intelligence); self-organising feature maps; vector quantisation; adaptive learning; block activity; edge degradation; edge preserving vector quantization; image coding; neural nets; self-organizing map; Algorithm design and analysis; Artificial neural networks; Clustering algorithms; Degradation; Discrete cosine transforms; Equations; Image coding; Neural networks; Testing; Vector quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN
0-7803-1421-2
Type
conf
DOI
10.1109/IJCNN.1993.716764
Filename
716764
Link To Document