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Image Compression is a method, which reduces the size of the data or the amount of space required to store the data. Digital image in their raw form require an enormous amount of storage capacity. There are various transformation techniques used for data compression. Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are the most commonly used transformation. The Discrete cosine transform (DCT) is a method for transform a signal or image from spatial domain to frequency component. DCT has high energy compaction property and requires less computational resources. On the other hand, DWT is multi resolution transformation. In this paper, we propose a hybrid DWT-DCT algorithm for image compression and reconstruction taking benefit from the advantages of both algorithms Keywords: Compression technique, DCT, DWT, hybrid transform, PSNR I.INTRODUCTION Image compression is very important for efficient transmission and storage of images. Demand for communication of multimedia data through the telecommunications network and accessing the multimedia data through Internet is growing explosively. With the use of digital cameras, requirements for storage, manipulation, and transfer of digital images, has grown explosively .These image files can be very large and can occupy a lot of memory. A gray scale image that is 256 x 256 pixels have 65, 536 elements to store and a typical 640 x 480 color image have nearly a million. Downloading of these files from internet can be very time consuming task. Image data comprise of a significant portion of the multimedia data and they occupy the major portion of the communication bandwidth for multimedia communication. Therefore for the development of efficient techniques image compression has become quite necessary. The image compression technique most often used is transform coding. Transform coding is an image compression technique that first switches to the frequency domain, then does its compressing. The transform coefficients should be decor related, to reduce redundancy and to have a maximum amount of information stored in the smallest space. These coefficients are then coded as accurately as possible to not lose information. In this research, we will use transform coding. Two fundamental components of compression are redundancy and irrelevancy reduction. Redundancies reduction aims at removing duplication from the signal source(image/video). Irrelevancy reduction omits parts of the signal that will not be noticed by the signal receiver.

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In this research work, a new automated system is developed for brain tumor detection by using Magnetic Resonance Imaging (MRI) on the basis of machine learning techniques. The major concerns in the brain tumor detection are time consuming, and the classification accuracy dependsonly on clinician’s experience. To address these issues, a new supervised system is developed for brain tumor detection. In this research study, a new segmentation approach was used for improving the brain tumor detection performance and to diminish the complexity of the system. Initially, Anisotropic Diffusion Filter (ADF) was used as an image pre-processing technique for removing noise from the collected brain image. Then, Berkeley Wavelet Transformation (BWT) was utilized for converting the spatial form of pre-processed MRI image into temporal domain frequency. Besides, Support Vector Machine (SVM) was usedas a classification technique to classify the normal and abnormal regions. SVM classifier effectively diminishes the size of resulting dual issue by developing a relaxed classification error bound. In addition, the undertaken classification approach quickly speed up the training process by maintaining a competitive classification accuracy. From the experimental analysis, the proposed system improved dice coefficient >0.9 compared to the existing systems. The experimental investigation validated and evaluated that the proposed system showed good performance related to the existing systems in light of dice coefficient and accuracy.