IMAGE COMPRESSION USING DISCRETE COSINE TRANSFORM

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ABSTRACT
Image Compression addresses the problem of reducing the amount of data required to represent the digital image. Compression is achieved by the removal of one or more of
three basic data redundancies: (1) Coding redundancy, which is present when less than optimal (i.e. the smallest length) code words are used; (2) Interpixel redundancy, which results from correlations between the pixels of an image & (3) psycho visual redundancy which is due to data that is ignored by the human visual system (i.e. visually
nonessential information). Huffman codes contain the smallest possible number of code symbols (e.g., bits) per source symbol (e.g., grey level value) subject to the constraint that the source symbols are coded one at a time. So, Huffman coding when combined with technique of reducing the image redundancies using Discrete Cosine Transform (DCT)
helps in compressing the image data to a very good extent.
The Discrete Cosine Transform (DCT) is an example of transform coding. The current JPEG standard uses the DCT as its basis. The DC relocates the highest energies to the upper left corner of the image. The lesser energy or information is relocated into other areas. The DCT is fast. It can be quickly calculated and is best for images with smooth edges like photos with human subjects. The DCT coefficients are all real numbers unlike the Fourier
Transform. The Inverse Discrete Cosine Transform (IDCT) can be used to retrieve the image from its transform representation. The Discrete wavelet transform (DWT) has gained widespread acceptance in signal processing and image compression. Because of their inherent multiresolution nature, waveletcoding schemes are especially suitable for applications where scalability and tolerable degradation are important. Recently the JPEG committee has released its new image coding standard, JPEG2000, which has been based upon DWT CHAPTERI
INTRODUCTION
1.1Discrete Cosine Transform
1.1.1 DISCRIPTION
Compressing an image is significantly different than compressing raw binary data. Of course,
general purpose compression programs can be used to compress images, but the result is less than optimal. DCT has been widely used in signal processing of image. The onedimensional DCT is useful in processing onedimensional signals such as speech waveforms. For analysis of two dimensional (2D) signals such as images, we need a 2D version of the DCT data, especially in coding for compression, for its nearoptimal performance. JPEG is a commonly used standard method of compression for photographic images. The name JPEG stands for Joint Photographic Experts Group, the name of the committee who created the standard. JPEG provides for lossy compression of images. Image compression is the application of data compression on digital images. In effect, the objective is to reduce redundancy of the image data in order to be able to store or transmit data in an efficient form. The best image quality at a given bitrate (or compression rate) is the main goal of image compression. The main objectives of this paper are reducing the image storage space, Easy maintenance and providing security, Data loss cannot effect the image clarity, Lower bandwidth\requirements for transmission, Reducing cost.
1.1.2 WHAT IS DCT
A discrete cosine transform (DCT) expresses a sequence of finitely many data points in terms of a sum of cosine functions oscillating at different frequencies. DCTs are important to numerous applications in science and engineering, from lossy compression of audio and images (where small highfrequency components can be discarded), to spectral methods for the numerical solution of partial differential equations on, it turns out that cosine functions are much more efficient (as explained below, fewer are needed to approximate a typical signal, whereas for differential equations the cosines express a particular choice of boundary conditions. In particular, a DCT is a Fourierrelated transform similar to the discrete Fourier transform (DFT), but using only real numbers. DCTs are equivalent to DFTs of roughly twice the length, operating on real data with even symmetry (since the Fourier transform of a real and even function is real and even), where in some variants the input and/or output data are shifted by half a sample. There are eight standard DCT variants, of which four are common.
The most common variant of discrete cosine transform is the typeII DCT, which is often called
simply "the DCT"; its inverse, the typeIII DCT, is correspondingly often called simply "the
inverse DCT" or "the IDCT". Two related transforms are the discrete sine transform (DST), which is equivalent to a DFT of real and odd functions, and the modified discrete cosine transform (MDCT), which is based on a DCT of overlapping data. 

