International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 2 Issue: 9 2824 – 2832 _______________________________________________________________________________________________________ 2824 IJRITCC | September 2014, Available @ http://www.ijritcc.org _______________________________________________________________________________________ Hybrid Techniques On Color And Multispectral Image For Compression C. Senthilkumar * Associate Professor & Ph.D (Scholar) Department of Computer Science, Erode Arts & Science College (Autonomous), Erode, Tamil Nadu, India csincseasc@gmail.com Dr. S. Pannirselvam Research Supervisor & Head Department of Computer Science, Erode Arts & Science College (Autonomous), Erode, Tamil Nadu, India pannirselvam08@gmail.com Abstract—Image Compression is a technique to reduce the number of bits required to represent and store an image. This technique is also used to compress two dimensional color shapes without loss of data as well as quality of the Image. Even though Simple Principal Component Analysis can apply to make enough compression on multispectral image, it needs to extend another version called Enhanced PCA(E-PCA). The given multispectral image is converted into component image and transformed as Column Vector with help of E-PCA. Covariance matrix and eigen values are derived from vector. Multispectral images are reconstructed using only few principal component images with the largest variance of eigen value. Then the component image is divided into block. After finding block sum value, mean value, the number of bits required to represent an image can be reduced by E-BTC model. The features are extracted and constructed in Table form. The proposed algorithm is repeated for all multispectral images as well as color image in the database. Finally, compression ratio table is generated. This proposed algorithm is tested and implemented on various parameters such as MSE, PSNR. These experiments are initially carried out on the standard color image and are to be followed by multispectral imager using MATLAB. Keywords— Enhanced Principal Component Analysis(E-PCA), multispectral image, component image, eigen value, eigen vector, SV, MV, E-BTC, Mean Square Error(MSE), Peak Signal to Noise Ratio(PSNR). __________________________________________________*****_________________________________________________ I. INTRODUCTION Multispectral image are images of the same object taken in different bands of visible or infrared region of the electromagnetic spectrum. Images are acquired for remote sensing applications are generally known as multispectral in nature. Multispectral image typically contains information outside the normal human perceptual range[27]. This type of image may include infrared, ultraviolet, x-ray, acoustic or radar data. These are not images in the usual sense because the information is not directly visible by the human system. If more than three bands of information are in the multispectral images, the dimensionality is reduced by applying PCT [Principal Component Transform]. Most of the satellites currently in orbit collect image information in 2 to 12 spectral bands; Source of these types of images include underwater sonar system, airbone radar, infrared imaging, medical diagnostics imaging system. The Principal Component Analysis(PCA) is one of the most successful technique that has been used in image recognition and compression. It is also used for data reduction and feature extraction analysis[3]. Data compression has been a major issue in today‟s everywhere commodity computing and