Course Content

Image Compression is a method of reducing the amount of data that we require to represent the image. Image Compression has been the most useful and very successful technologies in the field of digital image processing. Researchers have been using oversampling of images till recently. In the implementedsystem, architecture is implementedthat compresses the images using decimation of pixels. The image is prefiltered using a low-pass prefiltering process before pixel decimation to get redefined edges. In the resulting decimated image blocking artifacts are reduced, hence we can get an image that can be compressed and transmitted without any significant change to current image coding standards and systems.For the decompression procedure, the low resolution image is first decompressed then it is upscaled to its original resolution using image upscaling method and then applying edge enhancement operation. The implemented approach of pixel decimation outperforms JPEG in PSNR measure and achieves superior visual quality.

I INTRODUCTION

Image processing is field where the digital images are analysed or processed or manipulated in such a manner that the resultant image is a better quality image. Image compression is a methodology that is used to extract only the important information that is required to represent an image. This methodology helps reduce the file size of the image which conserves the memory resource and the bandwidth resource which are the limited resources available. Image compression technique has been a boon to the technology where a large number of images have to be stored and transmitted every second. Image compression makes it easier to transmit images using very less bandwidth and store those compressed images in a very less memory space.

Image compression is divided into two compression techniques namely lossy compression method and lossless compression method. The lossy compression technique is the one in which the decompressed image losses some of the important information which is required to represent the image. the lossless compression technique is the one in which the important information needed to represent the image is conserved and not lost. In other words there is no loss of data while the image is compressed. The quality for visualization of the image is the same as the original.

In this paper, the proposed technique is nearly lossless compression technique. The quality for visualization of the image is not affected when the image is reconstructed. The fence-downsampling process is used here which helps to reduce the redundant data in the image. Once the downsampling is done, the fence-downsampled image is processed further by applying the third party compression technique which first divides the image into 8×8 matrices. Then each 8×8 matrix is processed separately. This is done so, so that there will be no computational overhead which would have been so if whole image is processed at once.

The 8×8 matrix is transformed using discrete cosine transform. Periodic function is the sum of sines and/or cosnines of various frequencies, each multiplied by different coefficients. This series is the fourier series. In the discrete cosine transform, only the cosine is used where the real part of the fourier series is present. Sine consists of imaginary part of the fourier series. Discret cosine transform(DCT) is used to transform the image from spatial/time domain to frequency domain .

The reason why the discrete cosine transform has been used is that for a typical image, colors and the intensity values do not change abruptly between pixels and hence the transformation on observation shows that most of the information about the image which is visually significant is concentrated in just few coefficients of the dct. One of the advantages of using dct is that it can be reconstructed completely via an inverse process, with no loss of information.

Human eye is quite good at observing slight difference in brightness over a relatively large area, but it is not that good at differentiating the exact strength of a high frequency brightness variation. This allows the user to reduce the amount of information required to represent an image by ignoring the high frequency components. Then the quantized matrix is entropy encoded.

The encoder presented in this paper is the run length encoder. The run length encoder is a lossless encoder. No information is lost while encoding the quantized image. The scanning order used here is the zigzag order scan. The result when the run length encoder

is executed is two one-dimensional arrays that contain the image pixels and pixel count respectively.