WAVELET BASED IMAGE COMPRESSION

Wavelet is a recently developed compression technique in image compression. In this study, after multiple level 2-D wavelet transform of images, approximation and detail coefficients are obtained. These coefficients are quantized that more important coefficients are represented with higher accuracy and retained coefficients are represented with less accuracy or they are neglected. Afterward the quantized coefficient (symbols) is coded in a bit stream using recursive splitting Huffman coding. The quality of reconstructed images is measured with PSNR value, and compression ratio is calculated using given formula. Generally wavelet performs better Discrete Wavelet Transform (DWT) for image compression using MATLAB. In this approach we use JPEG2000 standard for both lossy and lossless compression. In order to reduce complexities of the design linear algebra view of DWT has been used in this concept. KeywordsDWT, MATLAB I.INTRODUCTION During last decade there has been enormous increase in digital images. This type of information gives rise to high transmission and storage cost. To store these images or make them available over networks, compression techniques are needed. Image compression plays a critical role in telematics applications, where it is desired that either single images or sequences of images be transmitted over computer networks at large distances so as that they could be used for a multitude of purposes. Image compression is a fast paced and dynamically changing field with many different varieties of compression methods available. Images contain large amount of data hidden in them, which is highly correlated. A common characteristic of most images is that the neighbouring pixels are correlated and therefore contain redundant information. There are three types of redundancy that can be exploited by image compression systems.  Spatial redundancy; in almost all natural images the values of neighbouring pixels are strongly correlated.  Spectral redundancy; in images composed of more than one spectral band the spectral values for the same pixel location are often correlated.  Temporal redundancy; Adjacent frames in videosequence often show very little change. Research advances in wavelet theory have created a surge of interest in applications like image compression. Mainly compression methods can be divided into two classes; lossless and lossy compression techniques:  Lossless compression guarantees thêiginal signal can be reconstructed without any errors. This is important for application like compression of text or medical images.  Lossy compression gives higher compression rates. But exact data cannot be reconstructed. Human visual system is not sensitive or has low sensitivity to some kind of errors. That’s why the compression potential is much higher when small reconstruction errors are allowed. The investigation and design of computationally efficient and effective software algorithms for lossy image compression forms the primary objective of this thesis. II. IMAGE QUALTY MEASUREMENT The two most prominently used parameters that define the quality of the image are:  Compression Ratio (CR) can be defined by the following formula: Compression Ratio= − [ . h . ] × (1)  Peak Signal to Noise Ratio (PSNR) can be defined by the following formula: Peak Signal to Noise Ratio = log MX M E (2) Where: