A discrete wavelet transform is one of the effective methodologies for compressing the image data and extracting the major characteristics from various data, but it always requires a number of target data composed of a power of 2. To overcome this difficulty without losing any original data information, we propose here a novel approach based on the Fourier transform. The key idea is simple but effective because it keeps all of the frequency components comprising the target data exactly. The raw data is firstly transformed to the Fourier coefficients by Fourier transform. Then, the inverse Fourier transform makes it possible to the number of data comprising a power of 2. We have applied this interpolation for the wind vector image data, and we have tried to compress the data by the multiresolution analysis by using the three-dimensional discrete wavelet transform. Several examples demonstrate the usefulness of our new method to work out the graphical communication tools.
One of the distinguished properties of the discrete wavelets transform is that the major dominant factors can be extracted from the data. We have applied this property to the data compression and reducing the noise data. In the present paper, we have tried to shrink and enlarge the wind vector image data by the three dimensional discrete wavelets transform. Several examples demonstrate the usefulness of our new method to work out the graphical communication tools.
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