Computer Engineering and Intelligent Systems www.iiste.org ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online) Image Compression Using Haar Wavelet Transform Nidhi Sethi, Department of Computer Science Engineering Dehradun Institute of Technology, Dehradun Uttrakhand , India Email:nidhipankaj.sethi102@gmail.com Telephone:91-9634702552 Ram Krishna, Department of Electrical Engineering Dehradun Institute of Technology, Dehradun Uttrakhand , India Email: rk_nedes@yahoo.co.in Prof R.P. Arora Dept of Computer Science Engineering Dehradun Institute of Technology, Dehradun Uttrakhand , India Email:rp_arora37@yahoo.co.in Abstract Compressing an image is significantly different than compressing raw binary data. General purpose compression programs can be used to compress images, but the result is less than optimal. This is because images have certain statistical properties which can be exploited by encoders specifically designed for them. Also, some of the finer details in the image can be sacrificed for the sake of saving a little more bandwidth or storage space. This also means that lossy compression techniques can be used in this area. The discrete wavelet is essentially sub band–coding system and sub band coders have been quite successful in speech and image compression. In this paper we have implemented HAAR Wavelet Transform. The results in terms of PSNR(Peak Signal Noise Ratio) and MSE (Mean Square Error)show that the Haar transformation can be used for image compression. The quantization is done by dividing the image matrix into blocks and taking mean of the pixel in the given block. It is clear that DWT has potential application in the compression problem and the use of Haar transform is ideally suited. Keywords: Wavelet transforms, Image compression, Haar wavelet, PSNR, MSE. I. Introduction One of the important factors for image storage or transmission over any communication media is the image compression. Compression makes it possible for creating file sizes of manageable, storable and transmittable dimensions. A 4 MB image will take more than a minute to download using a 64kbps channel, whereas, if the image is compressed with a ratio of 10:1, it will have a size of 400KB and will take about 6 seconds to download. Image Compression techniques fall under 2 categories, namely, Lossless and Lossy. In Lossless techniques the image can be reconstructed after compression, without any loss of data in the entire process. Lossy techniques, on the other hand, are irreversible, because, they involve performing quantization, which results in loss of data. Some of the commonly used techniques are Transform coding, (Discrete Cosine Transform, Wavelet Transform, Gabor Transform), Vector Quantization, Segmentation and Approximation methods, Spline approximation methods (Bilinear Interpolation/Regularisation), Fractal coding etc.. Wavelet Transform has received a great amount of attention in the last decade. Wavelet based image compression introduces no blocky artifacts in the decompressed image. The decompressed image is much smoother and pleasant to eyes. Also, we can achieve much higher compression ratios much regardless of the amount of compression achieved. Another interesting feature of wavelet is that we can improve the quality of the image by adding detail information. This feature is attractive for what is known