Wavelet compression has been shown to give exceptional subjective image quality with high compression ratios for medical imaging. In an effort to effect real-time wavelet compression of digitized ultrasound video for low bandwidth networks, Fourier domain subsampling may demonstrate reduced computational overhead compared to convolution methods. The anticipated benefit is dependant on: the size of the mother wavelet used, data dimensions along each axis, and available Fourier processing power. The process of wavelet compression is computationally expensive, requiring multiple convolutions with similar mother wavelets at different resolutions. In contrast, Fourier domain subsampling states that if an image is downsampled by a factor of two, the spatial frequencies of the image all increase by a factor of two. This allows the use of only one forward FFT on the data at run time, and only one inverse FFT at the time of each filter application, significantly reducing the computational load. A wavelet transform into the third dimension (time) takes advantage of the high correlation between adjacent frames in ultrasound video. Our presentation will demonstrate a comparison of benchmarks for both wavelet transform methods and analyze the advantage with respect to mother wavelet size.
