Comparative Study between Various Algorithms of Image Compression Techniques using MODIS Image

Compression techniques have a vital role to minimize the cost of information storage and/or transmission. In particular, techniques of image compression make the use of visual perception and statistical properties of image information to provide superior performance in comparison with the other conventional compression techniques. However, high-resolution images may face many compression challenges, especially when hiding them as embedding stage information. In this paper, the performance of four different compression techniques was evaluated and compared. These four different compression techniques are Discrete Cosine Transform “DCT”, Singular Value Decomposition (SVD), Block Truncation Coding (BTC), and Gaussian pyramid (GP) bench-mark techniques. Comparison results between objects composed of high- and medium-resolution imaging spectroradiometer (MODIS) images are presented, using signal-to-noise ratio (PSNR), correlation (CORR), mean square error (MSE), and structural similarity index (SSIM) as metrics to perform the test, it was found that BTC has the best performance in image recovery compared to other comparative techniques.