Research On The Approaches Of DCT-Based Image Compression And Quality Assessment

The past decade has seen an explosive growth in the amount of digital image and video content generation and sharing. This development has attributed to the massive advances made in image and video acquisition, compression, storage and communication technologies. Visual communication has now become a dynamic mode of information exchange. The propagation of multimedia content has affected our lives more than ever before. As an example, it would not be an exaggeration to claim that multimedia communication has become a major driver of services provided by wireless network service providers, or that web sites such as Youku now offer a new dimension of communication. Multimedia communication in general, spans audio, image and video communication. The amount of digital image and video content is being generated and shared has grown explosively in the recent years. The primary goal of image and video communication systems is to achieve the best possibly visual quality at a given rate constraint and channel conditions. Currently, the focus is limited to image communication systems. In order to optimize the components of the communication system to maximize perceptual quality, it is important to use a good measure of quality. Even though this fact bas been long recognized, the mean squared error(MSE), which is consider not the accurate one for measurement of perceptual quality, yet it has been a popular choice in the design of various components of an image communication system. Recent developments in the field of image quality assessment(IQA) have resulted in the development of powerful new algorithms. A few of these new algorithms include the structural similarity(SSIM) index, the visual information fidelity(VIF) criterion, and the visual signal to noise ratio(VSNR).In this dissertation, Digital Image compression principle has examined using DCT algorithm and a new algorithm has developed using blockproc function. Image compression principally reduces the size of file without compromising the image quality. Efforts for making compression process as good as the reconstructed image look close to original image, are always remains a challenging topic for the researchers. The Discrete cosine transform(DCT) is the most famous technique in this regard and extensively used for compression images. It has observed that, in DCT compression algorithm, rarely the reconstructed image shows haze or blur, specially dealing with large size of images. To overcome on these issues and rectify haze or blurring particles there are many techniques and algorithms. In this thesis, we are going to examine a compression technique using blockproc function. Addition of blockproc function in DCT algorithm may be a new approach and can use as deblurring tool in image processing. Blockproc processing approach extends logically to arbitrarily large images. It supports a file-to-file workflow so no need the input or the output image to be contained entirely in memory. The processing of each block is an independent operation and processing lends it to parallel processing. As compared to DCT algorithm, this new approach reduced the reconstructed image file size more precisely with better compression ratio. We made two Matlab codes using DCT and blockproc for compression and then compared the results based on, Image quality, Image size compression ratio, Peak Signal to Noise Ratio(PSNR), Mean Square Error(MSE) and Structural Similarity Based(SSIM) Image quality assessment. Hence, using compression through DCT along with blockproc is the best approach in Image processing. Moreover, it is a modern approach. It is very helpful for further studies related to compression of large images applications.