An Enhanced Satellite Image Compression Using Hybrid (DWT, DCT and SVD) Algorithm

Moko Anasuodei,Onuodu Friday Eleonu

doi:10.11648/j.ajcst.20210401.11

Abstract

Storing images consumes a lot of storage space due to the large number of bits used to represent them. These bits are comprised of pixels that make up the image. These heavy images are also very difficult to be transmitted over channels due to their great size. Compression involves the reduction of the amount of bits used in representing an image and consequently reducing the size of that image without losing any detail from the image. There are so many image compression techniques used to achieve this feat, but they have drawbacks such as lack of a model that can compress a satellite image, lack of adaptive reversible techniques for compression and inability to compress complex images such as satellite images. This work, proposed an hybrid Discrete Wavelet Transform, Discrete Cosine Transform and Singular Value Decomposition (DCT-DWT-SVD)-based techniques for satellite image compression. The algorithms were combined to breakdown the images into blocks/matrices and assign certain values to them depending on the concentration of colour bits around the region. The areas with higher bits are reduced and compression is achieved. A hybrid methodology of Agile and Waterfall model was used in this approach. The model was implemented using MATLAB and satellite images gotten from a public repository. The Compression ratio was 0.9990 and 0.9941 for the two images compressed which shows high and efficient compression. The Mean Square Error (MSE) was 2.51 which is low. This study will be beneficial to remote sensor companies, Graphic designers and the research community.

Highlights

Images are widely used in several applications for problem solving
The images are sometimes used in these modern technologies to promote user friendliness in the applications, or as outputs from these applications
The proposed method provided significantly better compression than JPEG and other DCTbased techniques

Summary

Introduction

Images are widely used in several applications for problem solving. The images are sometimes used in these modern technologies to promote user friendliness in the applications, or as outputs from these applications (such as in medical systems for representing Xray and scan results). Outputs represented in a pictorial form are usually more interpreted and understood than complex mathematical equations and logic. Most images are usually recorded in digital format and stored in several storage devices such as disks etc. The smaller the size of the image in terms of bits used to store it. Digital images are made up of pixels which are later represented by bits during storage. The pixels represent the colours on the image. Most of the images contain a significant amount of redundancy which makes their sizes very large and result in clumsy processing and storage. Compression techniques were introduced to remove the redundancies and reduce the image size while still retaining the quality of the image [1]

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