ISSN: 2277-9655 [Hamid* et al., 6(5): May, 2017] Impact Factor: 4.116 IC™ Value: 3.00 CODEN: IJESS7 http: // www.ijesrt.com © International Journal of Engineering Sciences & Research Technology [742] IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY TWO STEP TECHNIQUES FOR IMAGE COMPRESSION Fizza Hamid*, Rajneesh Talwar * M.Tech. Research Scholar, Department of ECE, CGCTC, Jhanjeri, Mohali, Punjab, India Principal, CGCTC, Jhanjeri, Mohali, Punjab, India DOI: 10.5281/zenodo.801331 ABSTRACT The image compression is the technique which is applied to reduce size of the original image. The image compression can be classified into lossy and loss-less type of compressions. The WDR is the lossy type of compression in which unwanted pixels will be removed from the image. In this work, improvement in the WDR algorithm is been proposed using the algorithm of decision tree. The decision tree is constructed of according to the similarity between the pixels. The pixels which have least similarity with the other pixels will be removed from the image. The simulation is been performed in MATLAB and it is been analyzed that PSNR, MSE and compression ratio parameters are improved with the proposed technique. KEYWORDS: WDR, Decision Tree, Similarity, Compression. INTRODUCTION The objective of image compression is to reduce irrelevance and redundancy of the image data keeping in mind the end goal to have the capacity to store or transmit data in an efficient form. Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level [1]. The reduction in file size permits more images to be stored in a given amount of disk or memory space. It likewise reduces the time required for images to be sent over the Internet or downloaded from Web pages. There are a few different ways in which image files can be compressed. Different methods for image compression incorporate the utilization of fractals and wavelets. These methods have not gained widespread acceptance for use on the Internet as of this writing. Be that as it may, both methods offer promise because they offer higher compression ratios than the JPEG or GIF methods for a few sorts of images [2]. Another new method that may in time supplant the GIF format is the PNG format. A text file or program can be compressed without the introduction of errors, however just up to a specific extent. This is called lossless compression. Beyond this point, errors are introduced. In text and program files, it is crucial that compression be lossless because a single error can seriously damage the meaning of a text file, or cause a program not to run. In image compression, a small loss in quality is generally not noticeable [3]. The reason why an image can be compressed is that the correlation between one pixel and its neighbor pixels is high, or one can state that the values of one pixel and its adjacent pixels are fundamentally the same. Once the correlation between the pixels is reduced, one can exploit the statistical characteristics and the variable length coding theory to reduce the storage quantity [4]. This is the most essential part of the image compression calculation; there are a lot of relevant processing methods being proposed. The JPEG has been the most common image format on the web for a long time. It is capable of retaining high caliber with small file sizes. Its ability to pack so much visual information into a small file is to a great extent because of exploiting the capacities, or rather limitations, of the human eye [5]. The Discrete Cosine Transformation (from this point forward alluded to as DCT) resembles a discrete Fourier transform in that it turns the spatial domain of an image into its frequency domain. The objective of quantization is to reduce the precision and to accomplish higher compression ratio. After the quantization has been connected to the image, a symbol encoding system is connected to the image [6]. Entropy is the measure of information present in the data, and an entropy coder encodes the given set of symbols with the minimum number of bits required to represent them. Entropy coding techniques for the most part gives lossless compression.