期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2014
卷号：7
期号：3
页码：453-474
DOI：10.14257/ijsip.2014.7.3.37
出版社：SERSC
摘要：With the growing demand of high quality multimedia (HD) the data size has increased thus the compression is the essential requirement to process and store data with smaller size. The Multiple Parameter Discrete Fractional Fourier Transform (MPDFRFT) is generalization of the discrete fractional Fourier Transform and can be use for compression of high resolution images with the extra degree of freedom provided by the MPDFRFT and its different fractional orders finally decompressed image can also be recovered. This paper deals with the image compression based on MPDFRFT using Eigen vector decomposition algorithm. The MPDFRFT possesses all the desired properties of discrete fractional Fourier transform. The MPDFRFT converts to the DFRFT when all of its order parameters are the same. We exploit the properties of multiple-parameter DFRFT and propose a novel compression scheme for satellite and medical images more conveniently than urban, rural and natural images. In this scheme image is subdivided and MPDFRFT is applied for the subdivided image to form transformed coefficients and Inverse MPDFRFT is applied for reconstruction of original images. The proposed compression scheme with MPDFRFT significantly shows better results over fractional cosine transform (FRCT), Fourier transforms (FT) and cosine transforms (CT). A comparison has been made between these techniques and observed that a good fidelity of decompressed image can be achieved at different fractional order parameter values of the transforms. The performance of system analyzed based on parameters like Peak Signal-to-Noise Ratio (PSNR), mean square error (MSE) and Compression Ratio (CR). The MPDFRFT provides better mean square error (MSE) and peak signal noise ratio (PSNR) for the same compression ratio (CR) as compared to FRCT, FT, cosine transform and classical lifting scheme based on wavelet, during image processing using MATLAB platform.
