This paper presents a new VQ technique called the SA-K algorithm which incorporates the simulated annealing mechanism into Kohonen's competitive learning to produce high quality codebooks. With a proper temperature schedule, the SA-K algorithm asymptotically becomes a descent competitive learning algorithm and both the centroid and the nearest neighbor conditions for optimality are satisfied, while the SA technique guarantees that the SA-K algorithm performs in a globally optimal manner. Experimental comparisons among the SA-K, Kohonen learning algorithm (KLA) and LBG algorithm for speech source data are given. The novel algorithm consistently shows the advantage over the KLA and LBG algorithm in the design of vector quantizers with different codebook sizes.<>
A new vector quantization algorithm based on simulated annealing
01.01.1994
246273 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
A New Vector Quantization Algorithm Based on Simulated Annealing
British Library Conference Proceedings | 1994
|Iterative sIB Algorithm based on Simulated Annealing
British Library Online Contents | 2010
|An adaptive hybrid vector quantization algorithm
AIAA | 1993
|Super-resolution algorithm based on weighted vector quantization
British Library Online Contents | 2014
|An Adaptive Hybrid Vector Quantization Algorithm
British Library Conference Proceedings | 1993
|