Image compression based on 2D Discrete Fourier Transform and matrix minimization algorithm

In the present era of the internet and multimedia, image compression techniques are essential to improve image and video performance in terms of storage space, network bandwidth usage, and secure transmission. A number of image compression methods are available with largely differing compression ratios and coding complexity. In this paper we propose a new method for compressing high-resolution images based on the Discrete Fourier Transform (DFT) and Matrix Minimization (MM) algorithm. The method consists of transforming an image by DFT yielding the real and imaginary components. A quantization process is applied to both components independently aiming at increasing the number of high frequency coefficients. The real component matrix is separated into Low Frequency Coefficients (LFC) and High Frequency Coefficients (HFC). Finally, the MM algorithm followed by arithmetic coding is applied to the LFC and HFC matrices. The decompression algorithm decodes the data in reverse order. A sequential search algorithm is used to decode the data from the MM matrix. Thereafter, all decoded LFC and HFC values are combined into one matrix followed by the inverse DFT. Results demonstrate that the proposed method yields high compression ratios over 98% for structured light images with good image reconstruction. Moreover, it is shown that the proposed method compares favorably with the JPEG technique based on compression ratios and image quality

IMAGE COMPRESSION USING WAVELETS

Image compression enables images for easier data storage and data transmission. One ofnewest technique used in compressing image is wavelet transform. Wavelet widely used in application such as medical imaging, internet imaging, scanning and printing, mobile and digital cameras. Wavelets are new filter that can keep the information in both time domain and frequency domain. The special about wavelet filter is that the window can be varied by changing the frequency. The objective of the project is to create a simulation model to investigate image compression using wavelets. The investigation will makes comparative study by applying different types of wavelet techniques on different types of images. The MATLAB software is used in doing simulation. As necessary background to do the project, basic concept of image processing, wavelet theory, image compression, and information theory are learned and discussed. The simulation will use several types of wavelets families including Haar, Daubachies, Symlet, Coiflet and Biorthogonal Spline wavelets. The papers will analyze and examine the effect of difference wavelet families, filter order, filter length, decomposition level and image content and quantizer type in compressing image. After doing numerous comparisons of wavelet effects on all test images, the results of the simulation shows that Daubachies wavelets family is having the most outstanding performance compared to other wavelet families. Hence, Daubachies is the bestfilter to usein doing wavelet image compression