Color Image Reduction using Genetic Algorithm

Er. Navneet Kaur; Er. Sukhpreet Kaur

Image reduction is one of the well known field which continually exploring new algorithms and techniques to reduce image, image reduction has many techniques RGB, HSV and many more images. One of the best known techniques is to reduce image using penalty functions, here in this research work image is reduced using penalty functions with the help of genetic algorithm, here GA is used to predict higher order equation for curve fitting to get reduced color image. Image reduction consists in reducing the dimension of the image while keeping as much information as possible. Image reduction can be used to accelerate computations on an image, or just to reduce the cost of its storage or transmission. This work present an RGB color image reduction which is based on genetic algorithm by approximating the value using a set of possible averaging functions component wise. This carry out an experimental study comparing different penalty functions and aggregation functions and analyzing the stability of the algorithms to different noises in the images. The methodology of the study is to take an image and compare that with the five previous algorithms:alg1, alg2, alg3, sub and arith with proposed work using genetic algorithm . Then reconstruct (enlarge) the image to its original size and compare the similarity between the original and reconstruction. Finally PSNR is calculated to measure the similarity between two images. PSNR is calculated after adding 10% of salt and pepper noise and 10% of gaussian noise to original image one by one.

Select Volume / Issues:

• Volume 6 Issue 2, Mar. 2015

Year:

2015

Type of Publication:

Article

Keywords:

Genetic Algorithm; Color Image Reduction; MSE; PSNR

Journal:

IJECCE

Volume:

6

Number:

2

Pages:

319-323

Month:

March