IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.10, October 2008 144 Manuscript received October 5, 2008 Manuscript revised October 20, 2008 Summary: The aim of this paper to examine a larger set of wavelet functions for implementation in a still image compression system using Set-Partitioning In Hierarchical Tree (SPIHT) algorithm. This paper discusses important features of wavelet transform in compression of still images, including the extent to which the quality of image is degraded by the process of wavelet compression and decompression. Image quality is measured objectively using peak signal to noise ratio. The effect of different parameters is studied on different wavelet functions. Key words: Wavelet transform, image coding, Hierarchical Tree, peak signal to noise ratio. 1 Introduction An image is a positive function on a plane. The value of this function at each point specifies the luminance or brightness of the picture at that point. Digital images are sampled versions of such functions, where the value of the function is specified only at discrete locations on the image plane, known as pixels. The value of the luminance at each pixel is represented to a pre-defined precision M. Eight bits of precision for luminance is common in imaging applications. The eight-bit precision is motivated by both the existing computer memory structures (1 byte = 8 bits) as well as the dynamic range of the human eye. The prevalent custom is that the samples (pixels) reside on a rectangular lattice which we will assume for convenience to be N × N. The brightness value at each pixel is a number between 0 and 2 M −1. The simplest binary representation of such an image is a list of the brightness values at each pixel, a list containingN2M bits. Our standard image example in this paper is a square image with 512 pixels on a side. Each pixel value ranges from 0 to 255, so this canonical representation requires 5122×8 = 2, 097, 152 bits. Image coding consists of mapping images to strings of binary digits. A good image coder is one that produces binary strings whose lengths are on average much smaller than the original canonical representation of the image. In many imaging applications, exact reproduction of the image bits is not necessary. In this case, one can perturb the image slightly to obtain a shorter representation. If this perturbation is much smaller than the blurring and noise introduced in the formation of the image in the first place, there is no point in using the more accurate representation. Such a coding procedure, where perturbations reduce storage requirements, is known as lossy coding. The goal of lossy coding is to reproduce a given image with minimum distortion, given some constraint on the total number of bits in the coded representation. Wavelet transforms are arguably the most powerful, and most widely used tool to arise in the field of signal processing .Their inherent capacity for multiresolution representation akin to the operation of the human visual system (HVS) motivated a quick adoption and widespread use of wavelets in image-processing applications. Indeed, wavelet based algorithms (EZW, SPIHT)[1,2] have dominated image compression for over a decade , and wavelet-based source coding is now emerging in other domains. Wavelets are increasingly used in the source coding of remote-sensing, satellite, and other geospatial imagery. A typical still image contains a large amount of spatial redundancy in plain areas where adjacent picture elements (pixels) have almost the same values. It means that the pixel values are highly correlated [3]. In addition, a still image can contain subjective redundancy, which is determined by properties of HVS. The redundancy can be removed to achieve compression of the image data. A basic measure for the performance of a compression algorithm is compression ratio (CR). Wavelet compression is a lossy compression scheme, the image compression algorithm should achieve a tradeoff between CR and image quality. Higher compression ratios will produce lower image quality and vice-versa. PERFORMANCE ANALYSIS OF IMAGE CODING USING WAVELETS G.Sadashivappa, Assistant Professor Dr. K.V.S.Ananda Babu Department of Telecommunication Engg Principal R.V.College of Engineering, Bangalore – 560 059 C.M.R. Institute of Technology, Bangalore – 560 037