Journal of Computer Science 10 (10): 2095-2104, 2014 ISSN: 1549-3636 © 2014 Science Publications doi:10.3844/jcssp.2014.2095.2104 Published Online 10 (10) 2014 (http://www.thescipub.com/jcs.toc) Corresponding Author: Nidhal K. El Abbadi, Department of Computer Science, University of Kufa, Najaf, Iraq 2095 Science Publications JCS IMAGE COMPRESSION BASED ON SVD AND MPQ-BTC 1 Nidhal K. El Abbadi, 2 Adil Al Rammahi, 2 Dheiaa Shakir Redha and 2 Mohammed Abdul-Hameed 1 Department of Computer Science, University of Kufa, Najaf, Iraq 2 Department of Mathematics, University of Kufa, Najaf, Iraq Received 2014-04-10; Revised 2014-04-11; Accepted 2014-06-30 ABSTRACT In this study we will provide a new way of images compression based on two mathematic concepts, these two concepts are Singular Value Decomposition (SVD) and Block Truncation Coding. The input image either is in JPEG format or in BMP format and the current way suitable for both color and gray scale images. The input image will be compressed first by reducing the image matrix rank, which achieved by using the SVD process and then the result matrix compressed by using BTC. The proposed algorithm gives further lossless compression to JPEG compression process and the rate of compression reach more than 99%. Keywords: SVD, BTC, Image Processing, Compression, MPQ-BTC 1. INTRODUCTION With the growth of using multimedia such as images, video, graphs and audio in different application and internet, large amount of data are transmitted through network. Using of these data increase the requirements of storage capacity and transmission bandwidth. Image compression means minimizing the size of files in bytes without degrading the quality of the image to an unacceptable level. The main task of compression is to remove the redundancy (Nivedita et al., 2012). Image compression deals with the problem of reducing the amount of data required to represent a digital image. Compression is achieved by the removal of three basic data redundancies: (1) Coding redundancy, which is present when less than optimal; (2) Inter-pixel redundancy, which results from correlations between the pixels; (3) Psycho-visual redundancies, which is due to data that is ignored by the human visual (Doaa and Chadi, 2011). There are two types of image compression namely lossy image compression and lossless image compression, the lossless compression mean the possibility to reconstruct the origin image and message without losing any data, while the lossy compression stand for the idea of losing some of information and data after decompressed the image. Lossless data compression is ideal for text (Santosh et al., 2011). Lossy compression yields good compression ratio comparing with lossless compression while the lossless compression gives good quality of compressed images. JPEG and Block Truncation Coding (BTC) is a lossy image compression techniques. It is a simple technique which involves less computational complexity. BTC is a recent technique used for compression of monochrome image data. It is one-bit adaptive moment preserving quantizer that preserves certain statistical moments of small blocks of the input image in the quantized output. There are some papers suggested to use the SVD with other compression techniques such as. Awwal et al. (2014) presented new compression technique using SVD and the wavelet difference reduction WDR. The WDR used for further reduction. This technique tested with other techniques such as WDR and JPEG 2000 and gives better result than these techniques. Using WDR with SVD enhance the PSNR and compression ratio. Adiwijaya et al. (2013) suggested new compression method based on Wavelet-SVD, which used a graph