Vector quantization and signal compression
Vector quantization and signal compression
Self-Organizing Maps
Image compression using self-organizing maps
Systems Analysis Modelling Simulation - Special issue: Digital signal processing and control
Real-Valued Pattern Classification Based on Extended Associative Memory
ENC '04 Proceedings of the Fifth Mexican International Conference in Computer Science
A Fast Search Algorithm for Vector Quantization Based on Associative Memories
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Design of an evolutionary codebook based on morphological associative memories
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Image compression by self-organized Kohonen map
IEEE Transactions on Neural Networks
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This paper presents a vector quantization algorithm for image compression based on extended associative memories. The proposed algorithm is divided in two stages. First, an associative network is generated applying the learning phase of the extended associative memories between a codebook generated by the LBG algorithm and a training set. This associative network is named EAM-codebook and represents a new codebook which is used in the next stage. The EAM-codebook establishes a relation between training set and the LBG codebook. Second, the vector quantization process is performed by means of the recalling stage of EAM using as associative memory the EAM-codebook. This process generates a set of the class indices to which each input vector belongs. With respect to the LBG algorithm, the main advantages offered by the proposed algorithm is high processing speed and low demand of resources (system memory); results of image compression and quality are presented.