https://doi.org/10.26706/ijceae.5.1.20240504

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Abstract:-

Compression process is very important for data transfer in information technology. The difficult part of the data compression process is keeping quality of data transferred at high compression ratio. In this research we introduce a new image compression process that uses both fractional Fourier transform and wavelet. While wavelets are the best method for feature extraction from the image, the low frequency of wavelet decomposition are the part in compression process that most of the present methods don’t touch it. On the other hand, fractional Fourier transform is a suitable and helps in the compressed coding of the image. Hence, we have used fractional Fourier transform to compress sub-bands of the wavelet. In this technique, an image is divided into low frequency and high frequency sub bands by using (Daubechies wavelet filter) and level one quantization for both low frequency and high frequency sub bands. The low-frequency sub bands are compressed by using Fourier transform with optimal fractional solution, and high-frequency sub bands are compressed by removing zeroes and storage only non-zero blocks and its position. The compressed wavelet coefficients are compressed by applied of level two quantization and kept as array. This array is programmed by using arithmetic encoder and followed by run length programing. The experimental results of the proposed technique with a different testing image are compared with some of the existing image compression techniques. The results show that the proposed technique has un important enhancement in reconstruction of image quality.

Index Terms:-

Image compression. Quantization. Sub bands. One dimensional discrete fractional Fourier transform (DFrFT). Scalability, Cost-effectiveness

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