Digital Image Compression and Decompression Using Three Different Transforms and Comparison of Their Performance
P. Sathyanarayana, Hamid R. Saeedipour · University of the South Pacific Electronic Research Repository (The University of the South Pacific) · 2006
Data compression is an important tool in digital image processing to reduce the burden on the storage and transmission systems. The basic idea of data compression is to reduce the number of the image pixel elements directly, say by sampling, or by using transforms and truncate the transformed image coefficients, so that the total number of picture elements or its coefficients are reduced. The image information now requires lesser storage and also lesser band width for transmission. When ever the image is to be recovered or received after transmission the image information is to be decompressed i.e. brought back to the original size and form. This compression process is essential for images taken by satellites, or unmanned aerial vehicles (UAVs) for remote sensing and weather application. By applying compression algorithm the image data may take one fourth or even less size with out loss of much information. There are different methods for compression and decompression process. In this paper three methods are used for both data compression and decompression process, they are i. Discrete Hartley type transform, ii. Fast Fourier transform (FFT), iii. Discrete cosine transforms (DCT). Algorithms are developed and tested using the three methods on different images. A comparison with respect to mean square error with the original image also presented. The main advantage of discrete Hartley type transform is, it is a real transform. Discrete cosine transform also has similar performance as that of discrete Hartley type transform. An algorithm for compression using FFT method is also presented.