Satellite Image Compression Algorithm Based on the FFT

Khaled Sahnoun,Noureddine Benabadji

doi:10.5121/ijma.2013.6106

Abstract

Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level ,the reduction in file size allows more images to be stored in a given amount of disk or memory space, it also reduces the time required for images to be sent over the ground This paper presents a new coding scheme for satellite images. In this study we apply the fast Fourier transform and the scalar quantization for standard LENA image and satellite image, The results obtained after the (SQ) phase are encoded using entropy encoding, after decompression, the results show that it is possible to achieve higher compression ratios, more than 78%, the results are discussed in the paper.

Highlights

Satellites image compression is used to minimize the amount of memory needed to represent this image [1], satellites images often require a large number of bits to represent them, and if the image needs to be transmitted to the ground or stored, it is impractical to do so without somehow reducing the number of bits for this data [2][3]
In this study we have developed a technique of satellites image compression based on the fast Fourier transform (FFT) “Fourier transform” and SQ “scalar quantization” to compression and decompression Satellites image, with satisfactory quality of the reconstructed image
Evaluation of compression Compression ratio (T%): Compression gain: Mean Squared Error: Peak Signal to Noise Ratio PSNR A powerful algorithm has a gain of maximum compression (T) and a minimum mean square error [7],the compression ratio and MSE error, PSNR are calculated by equations (8), (6) and (11)

Summary

INTRODUCTION

Satellites image compression is used to minimize the amount of memory needed to represent this image [1], satellites images often require a large number of bits to represent them, and if the image needs to be transmitted to the ground or stored, it is impractical to do so without somehow reducing the number of bits for this data [2][3]. The Fourier transform (FFT), known as frequency analysis or spectral involved in the implementation of many digital techniques for processing signals and images [4] It is found in applications such as direct harmonic analysis of musical signals and vibrations, and reduces the rate coding of speech and music, compression, and digital transmissions Applying a Fourier transform give a complex image [5], we calculated the module Fm and the phase Fp of the original image, and we represent the module. The variables u, v used in the equation (3) are variable frequency x, y used in the equation (4) are variable in the spatial domain, F(u,v) Is often represented by its amplitude and phase, the formula is given by:

PROPOSED APPROACH

EXPERIMENTAL RESULTS

CONCLUSIONS