An-Najah Staff

A new wavelet transform (WT) is introduced based on the fractional properties of the traditional Fourier transform. The new wavelet follows from the fractional Fourier order which uniquely identifies the representation of an input function in a fractional domain. It exploits the combined advantages of WT and fractional Fourier transform (FrFT). The transform permits the identification of a transformed function based on the fractional rotation in time-frequency plane. The fractional rotation is then used to identify individual fractional daughter wavelets. This study is, for convenience, limited to one-dimension. Approach for discussing two or more dimensions is shown.

Wavelet compression technique is widely used to achieve high good compression factor. In this paper, certain experiments were performed to identify the importance and the sensitivity of this technique to various internal and external parameters. Compression factor and SNR are the main factors to be optimized here against many factors like; image type, division factor, subblock size. Changing these parameters has shown a significant change in the performance leading to verified steps on how to choose these parameters in order to optimize the performance to get better compression and quality

Wavelet compression technique is widely used to achieve high good compression factor. In this paper, certain experiments were performed to identify the importance and the sensitivity of this technique to various internal and external parameters. Compression factor and SNR are the main factors to be optimized here against many factors like; image type, division factor, subblock size. Changing these parameters has shown a significant change in the performance leading to verified steps on how to choose these parameters in order to optimize the performance to get better compression and quality