A Novel Image Compression Technique with Multiple Parameter Discrete Fractional Fourier Transform for Satellite and Medical Imaging - Deepak Sharma; Narendra Singh; Jitendra Kanungo; Jitendra Raghuwanshi (Science and Technology: Developments and Applications Vol. 1, 2024)
Image and video data compression refers to a process in which the amount of data used to represent image and video is reduced to meet a bit rate requirement while the quality of the reconstructed image or video satisfies a requirement for a certain application and the complexity of computation involved is affordable for the application. With the growing demand for high quality multimedia (HD) the data size has increased thus compression is the essential requirement to process and store data with smaller sizes. The Multiple Parameter Discrete Fractional Fourier Transform (MPDFRFT) is a generalization of the discrete fractional Fourier Transform and can be used 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 by avoiding noise. This paper deals with image compression based on MPDFRFT using Eigenvector 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 which require data retention and need to preserve for noise and distortion with the help of MPDFRFT parameters. 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 transforms (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 the system was analyzed based on parameters like Peak Signal-to-Noise Ratio (PSNR), mean square error (MSE) and Compression Ratio (CR). The satellite defense image shows the maximum compressed image up to 60% with significant image quality with respect to the original image size and for medical error image shows a large decompressed size with 39% but medical error can be detected for smaller quality factor also and can be reconstructed with little larger size. 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.