An Effective Color Quantization Method Using Color Importance-Based Self-Organizing Maps

Title:

An Effective Color Quantization Method Using Color Importance-Based Self-Organizing Maps

Creator:

Park, Hyun Jun, Kim , Kwang Baek, and Cha, Eui Young

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:6738b947-a311-41db-a662-9ea3f5a8d217
uuid:6738b947-a311-41db-a662-9ea3f5a8d217
doi:10.14311/NNW.2015.25.006

Subject:

SOM, color quantization, and image processing

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Color quantization is an important process for image processing and various applications. Up to now, many color quantization methods have been proposed. The self-organizing maps (SOM) method is one of the most effective color quantization methods, which gives excellent color quantization results. However, it is slow, so it is not suitable for real-time applications. In this paper, we present a color importance{based SOM color quantization method. The proposed method dynamically adjusts the learning rate and the radius of the neighborhood using color importance. This makes the proposed method faster than the conventional SOM-based color quantization method. We compare the proposed method to 10 well-known color quantization methods to evaluate performance. The methods are compared by measuring mean absolute error (MAE), mean square error (MSE), and processing time. The experimental results show that the proposed method is effective and excellent for color quantization. Not only does the proposed method provide the best results compared to the other methods, but it uses only 67.18% of the processing time of the conventional SOM method.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2015 Volume:25 | Number:2

Harvested from:

CDK

Metadata only:

false