DocumentCode :
3629704
Title :
Kolmogorov complexity of spherical vector quantizers
Author :
Velimir M. Ilic;Zoran H. Peric
Author_Institution :
Accordia Group, U?itelj Tasina 38, 18 000 Ni?, Yugoslavia
fYear :
2008
Firstpage :
47
Lastpage :
52
Abstract :
In this paper, we investigate memory complexity of spherical vector quantizer from Kolmogorovpsilas perspective. The method for expressing the quantizer as binary string is proposed and minimal description length of the string is considered as Kolmogorov complexity of the quantizer. The Kolmogorov complexity is compared to memory requirements of two main algorithms for spherical vector quantizer design: uniform spherical quantizer and generalized Lloyd-Maxpsilas algorithm. It is proven that first of them has the minimal memory requirements needed for spherical quantizer realization, while the other upper bounds the theoretical minimal description length of the quantizer.
Keywords :
"Vector quantization","Image coding","Decoding","Neural networks","Algorithm design and analysis","Upper bound","Turing machines","Speech coding","Digital control","Audio coding"
Publisher :
ieee
Conference_Titel :
Neural Network Applications in Electrical Engineering, 2008. NEUREL 2008. 9th Symposium on
Print_ISBN :
978-1-4244-2903-5
Type :
conf
DOI :
10.1109/NEUREL.2008.4685558
Filename :
4685558
Link To Document :
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