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

The Market Value and Dynamic Interest Rate Risk of Swaps

At the time of initiation, interest rate swaps are of zero market value to the counterparties involved. However, as time passes, the market value of the swap position of each counterpart may become positive or negative. These value changes are stochastic in nature and are primarily driven by stochastic variations of the term structure of interest rates. In this paper, we develop models for determining the market values and dynamic interest rate risks of existing swap positions using the one-factor general equilibrium term structure model of Cox, Ingersoll, and Ross (1985). The valuation and risk measurement framework of this paper should be useful in developing a value turn risk accounting method advocated by Merton and Bodie (1995) for better internal management and reporting purposes and for more effective regulation. This paper was presented at the Financial Institutions Center's October 1996 conference on "

Joint image encryption and compression schemes based on hexa-coding

This research proposes a new image compression and encryption method depend on a modified JPEG technique combined with the Hexa-Coding algorithm. The compression algorithm starts by dividing an image into 8x8 blocks, then DCT (Discrete Cosine Transform) is applied to all blocks independently followed by uniform quantization. Additionally, the size of blocks is reduced by eliminating insignificant coefficients, and then Arithmetic coding is applied to compress residual coefficients. Finally, Hexa-encoding is applied to the compressed data to further reduce compression size as well as provide encryption. The encryption is accomplished based on five different random keys. The decompression uses a searching method called FMSA (Fast Matching Search Algorithm) which is used for decoding the previously compressed data, followed by Arithmetic decoding) to retrieve residual coefficients. These residuals are padded with zeros to rebuild the original 8x8 blocks. Finally, inverse DCT is applied to reconstruct approximately the original image. The experimental results showed that our proposed image compression and decompression has achieved up to 99% compression ratio while maintaining high visual image quality compared with the JPEG technique