Joint thresholding and quantizer selection for compression of medical ultrasound images in the wavelet domain

This paper introduces a simple and efficient technique for compression of medical ultrasound (US) images in the wavelet domain. The statistics of subband wavelet coefficients are modelled using the generalized Gaussian distribution (GGD). By exploiting these statistics, a uniform scalar quantizer is designed which adapts very well to the changing statistics of the signal across various subbands and scales. To increase the quantization performance, a threshold is chosen adaptively to zero-out the insignificant wavelet coefficients in the detail subbands before quantization. A distinctive feature of the proposed technique is that it unifies the two approaches to image adaptive coding: rate-distortion (R-D) optimized quantizer selection and R-D optimal thresholding, in order to increase the compression performance of the coder. The operational R-D criterion used for joint optimization is derived in the minimum description length (MDL) framework. The experimental results show that the joint R-D optimization leads to significant improvement in the compression performance of the proposed coder, named JTQ-WV, over the best state-of-the-art image coder, SPIHT. For example, the coding of US images at 0.25 bpp by JTQ-WV yields a PSNR gain of 1.0 dB over the benchmark SPIHT.