Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-05258-3_29 | MICAI |
Keywords | Field | DocType |
associative memories,training set,lbg algorithm,vector quantization algorithm,image compression,extended associative memory,associative network,proposed algorithm,input vector,lbg codebook,associative memory,vector quantization | Associative property,Content-addressable memory,Linde–Buzo–Gray algorithm,Computer science,Vector quantization,Artificial intelligence,Training set,Pattern recognition,Algorithm,Image coding,Machine learning,Image compression,Codebook | Conference |
Volume | ISSN | Citations |
5845 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enrique Guzmán | 1 | 18 | 3.19 |
| Oleksiy Pogrebnyak | 2 | 28 | 10.20 |
| Cornelio Yáñez | 3 | 16 | 3.31 |
| Pablo Ramírez | 4 | 1 | 4.74 |