Joint space-frequency segmentation, entropy coding and the compression of ultrasound images

Joint space-frequency segmentation is a relatively new image compression technique that finds the rate-distortion optimal representation of an image from a large set of possible space-frequency partitions and quantizer combinations. As such, the method is especially effective when the images to code are statistically inhomogeneous, which is certainly the case in the ultrasound modality. Unfortunately, however, the original paper on space-frequency segmentation neglected to use an actual entropy coder, but instead relied upon the zeroth-order entropy to guide the algorithm. In this work, we fill this gap by comparing actual entropy-coding strategies and their effect on both the resulting segmentations as well as the rate-distortion performance. We then apply the resulting "complete" algorithm to representative ultrasound images. The result is an effective technique that performs significantly better than SPIHT using both objective and subjective measures.