Abstract—The aim of this paper to characterize a larger set of wavelet functions for implementation in a still image compression system using SPIHT algorithm. This paper discusses important features of wavelet functions and filters used in sub band coding to convert image into wavelet coefficients in MATLAB. Image quality is measured objectively using peak signal to noise ratio (PSNR) and its variation with bit rate (bpp). The effect of different parameters is studied on different wavelet functions. Our results provide a good reference for application designers of wavelet based coder. Keywords—Wavelet, image compression, sub band, SPIHT, PSNR. I. INTRODUCTION ATA compression techniques help in efficient data transmission, storage and utilization of hardware resources. Uncompressed multimedia requires considerable storage capacity and transmission bandwidth. Despite rapid progress in mass- storage density, processor speeds and digital communication system performance, demand for data storage capacity and data transmission bandwidth continues to outstrip the capabilities of available technologies. The recent growth of intensive digital audio, image and video (multi media) based applications, have not only sustained the compression of such signals central to signal storage and digital communication technology. Table I shows the multimedia data types and its requirements. This information clearly suggests the need of compression. II. IMAGE COMPRESSION SCHEMES Image compression reduces the amount of data required to represent an image by removing redundant information. Three types of redundancies typically exist in digital images that can be exploited by compression. These are, coding redundancy that arises from the representation of the image gray levels, interpixel redundancy that exists due to high correlation between neighboring pixels, and psycho visual redundancy that is obtained based on Human perception of the image information [8]. An image compression system consists of an G. Sadashivappa is Assistant Professor in the Department of Telecommunication Engineering, R. V. College of Engineering, Mysore Road, Bangalore-59, Karnataka, India (e-mail:g_sadashivappa@yahoo.com). K.V.S. Ananda Babu is the Principal of C.M.R.Institute of Technology, No.132, AECS Layout; I.T.Park Road, Bangalore -37, Karnataka, India (e- mail:anandkvs@hotmail.com). encoder that exploits one or more of the above redundancies to represent the image data in a compressed manner, and a decoder that is able to reconstruct the age from the compressed data. The compression that is performed on images can either be lossless or lossy. Images compressed in a lossless manner can be reconstructed exactly without any change in the intensity values. This limits the amount of compression that can be achieved in images encoded using lossless techniques. However, many applications such as satellite image processing and certain medical and document imaging, do not tolerate any losses in their data and are frequently compressed using lossless compression methods. Lossy encoding is based on trading off the achieved compression or bit rate with the distortion of the reconstructed image. Lossy encoding for images is usually obtained using transform encoding methods. Transform domain coding is used in images to remove the redundancies by mapping the pixels into a transform domain prior to encoding. The mapping is able to represent image information containing most of the energy into a small region in the transform domain requiring only a few transform coefficients to represent. For compression, only the few significant coefficients must be encoded, while a majority of the insignificant transform coefficients can be discarded without significantly affecting the quality of the reconstructed image. An ideal transform mapping should be reversible and able to completely decor relate the transform coefficients. TABLE I MULTIMEDIA DATA Multimedia data Size/duration Bits/pixel or Bits/sample Uncompressed size Page of text 11” x 8.5” Varying resolution 16-32 Kbits Telephone quality speech 1 Sec 8 bps 64 Kb/sec Gray scale image 512 x 512 8 bpp 2.1 Mb/image Color image 512 x 512 24 bpp 6.29 Mb/image Medical image 2048 x 2048 12 bpp 100 Mb/image Full motion Video 640 x 640,10 Sec 24 bpp 2.21 Gbits Evaluation of Wavelet Filters for Image Compression G. Sadashivappa, and K. V. S. AnandaBabu D World Academy of Science, Engineering and Technology International Journal of Electronics and Communication Engineering Vol:3, No:3, 2009 430 International Scholarly and Scientific Research & Innovation 3(3) 2009 scholar.waset.org/1307-6892/3559 International Science Index, Electronics and Communication Engineering Vol:3, No:3, 2009 waset.org/Publication/3559