A Fast Search Algorithm for Vector Quantization Based on Associative Memories
One of the most serious problems in vector quantization is the high computational complexity at the encoding phase. This paper presents a new fast search algorithm for vector quantization based on Extended Associative Memories (FSA-EAM). In order to obtain the FSA-EAM, first, we used the Extended Associative Memories (EAM) to create an EAM-codebook applying the EAM training stage to the codebook produced by the LBG algorithm. The result of this stage is an associative network whose goal is to establish a relation between training set and the codebook generated by the LBG algorithm. This associative network is EAM-codebook which is used by the FSA-EAM. The FSA-EAM VQ process is performed using the recalling stage of EAM. This process generates a set of the class indices to which each input vector belongs. With respect to the LBG algorithm, the main advantage offered by the proposed algorithm is high processing speed and low demand of resources (system memory), while the encoding quality remains competitive.
Keywordsvector quantization associative memories image coding fast search
- 6.Bahram, S., Azami, Z., Feng, G.: Robust Vector Quantizer Design Using Competitive Learning Neural Networks. In: Proc. of European Workshop on Emerging Techniques for Communications Terminals, pp. 72–75. IEEE Press, Toulouse (1997)Google Scholar
- 7.Kohonen, T.: Automatic Formation of Topological Maps of Patterns in a Self organizing System. In: Oja, E., Simula, O. (eds.) Proc. 2SCIA, Scand. Conf. on Image Analysis, pp. 214–220. Helsinki, Finland (1981)Google Scholar
- 10.Nasrabadi, N., Feng, Y.: Vector Quantization of Images based upon the Kohonen Self-Organizing Feature Maps. In: IEEE International Conference on Neural Networks, vol. 1, pp. 101–108 (1988)Google Scholar
- 15.Barron, R.: Associative Memories and Morphological Neural Networks for Patterns Recall (in Spanish). PhD Thesis, Center for Computing Research (2005)Google Scholar