INTEGRATING EMBEDDED ZEROTREE AND SET PARTITIONING IN HIERARCHICAL TREES FOR WAVELET BASED IMAGE COMPRESSION

The need of era in image compression is to minimize the number of bits needed to represent the image for ease of storage and transmission. The image compression algorithm includes iterative phases of quantization, coding and decoding the transform processing. Several methods have been proposed in the past for performing image compression. The basic idea of any compression method is to compress and decompress a grayscale and/or truecolor image using some thresholding and encoding technique. The existing work in this paper is based on the use of various types of compression methods like EZW, SPIHIT, ASWDR, and WDR. The use of proposed progressive method starts with Embedded Zero tree Wavelet algorithm and Set partitioning in Hierarchical Trees algorithm using the Haar wavelet and the BIOR4.4 wavelet. The experimental results show that the efficiency of proposed system is higher than existing systems. The validation of the proposed method is done through quantitative metrics such as Peak Signal to Noise Ratio (PSNR), Compression Ratio (CR), Mean Square Error (MSE), Bit per pixels. The proposed algorithm yields high values for these metrics with better image quality.