This reduction is possible when the original dataset contains some type of redundancy. Data compression is defined as the process of encoding data using a representation that reduces the overall size of data. Image compression means reducing the size of graphics file, without compromising on its quality. The proposed algorithm yields high values for these metrics with better image quality. 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 experimental results show that the efficiency of proposed system is higher than existing systems. 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 existing work in this paper is based on the use of various types of compression methods like EZW, SPIHIT, ASWDR, and WDR. The basic idea of any compression method is to compress and decompress a grayscale and/or true-color image using some thresholding and encoding technique. Several methods have been proposed in the past for performing image compression. The image compression algorithm includes iterative phases of quantization, coding and decoding the transform processing. 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. For the implementation of this proposed work we use the Image Processing Toolbox under Matlab software. The numerical analysis of such algorithms is carried out by measuring Peak Signal to Noise Ratio (PSNR), Compression Ratio (CR). Images obtained with those techniques yield very good results. The above techniques have been successfully used in many applications. This paper focuses important features of transform coding in compression of still images, including the extent to which the quality of image is degraded by the process of compression and decompression. Block Truncating Coding, Wavelet, Fractal and Embedded Zero Tree image compression. We undertake a study of the performance difference of different transform coding techniques i.e. Fractal image compression has been widely used to compress the image. For this different compression algorithm are used to compress images.
Compressing an image is significantly different than compressing raw binary data. In this study Image compression was applied to compress and decompress image at various compression ratios. Image compression is fundamental to the efficient and cost-effective use of digital imaging technology and applications. Many different image compression techniques currently exist for the compression of different types of images.
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