Name
Abstract | ||
|---|---|---|
The vector quantization for color image requires the analysis of image pixels for determinating the codebook previously not known, and the self-organizing map (SOM) algorithm, which is the self-learning model of neural network, is widely used for the vector quantization(VQ). However, the vector quantization using SOM shows the underutilization that only some code vectors generated are heavily used. This defect is incurred because it is difficult to estimate correctly the center of data with no prior information of the distribution of data. In this paper, we propose an enhanced self-organizing vector quantization method for color images. The results demonstrated that compression ratio by the proposed method was improved to a greater degree compared to the conventional SOM algorithm. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-30501-9_39 | PDCAT |
Keywords | Field | DocType |
compression ratio,neural network,color image,vector quantization,self-organizing map,greater degree,enhanced self-organizing vector quantization,image pixel,conventional som algorithm,self organization | Linde–Buzo–Gray algorithm,Computer science,Learning vector quantization,Algorithm,Vector quantization,Pixel,Quantization (signal processing),Color quantization,Codebook,Color image | Conference |
Volume | ISSN | ISBN |
3320 | 0302-9743 | 3-540-24013-6 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jae-Hyun Cho | 1 | 20 | 11.08 |
| Hyunjung Park | 2 | 320 | 13.71 |
| kwangbaek kim | 3 | 110 | 43.94 |