Mosaic ultrasound medical image compression using TTA10 algorithm

Tsishkou, Dzmitry; Bovbel, E.I.

doi:10.1109/icdsp.2002.1028302

Cited by 1 publication

(1 citation statement)

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“…Data compression has been developed to mitigate the issue of data transfer in previous studies. Some of them simply transformed RF data into baseband data and suppressed baseband envelope data through discrete wavelet transform (DWT), 11,12 quantization, and arithmetic coding. In contrast, our previous studies attempt to compress the raw data based on JPEG and MPEG compression techniques.…”

Section: Introductionmentioning

confidence: 99%

Lossless Data Compression for Improving the Performance of a GPU-Based Beamformer

Lok

Fan

2014

Ultrason Imaging

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The powerful parallel computation ability of a graphics processing unit (GPU) makes it feasible to perform dynamic receive beamforming However, a real time GPU-based beamformer requires high data rate to transfer radio-frequency (RF) data from hardware to software memory, as well as from central processing unit (CPU) to GPU memory. There are data compression methods (e.g. Joint Photographic Experts Group (JPEG)) available for the hardware front end to reduce data size, alleviating the data transfer requirement of the hardware interface. Nevertheless, the required decoding time may even be larger than the transmission time of its original data, in turn degrading the overall performance of the GPU-based beamformer. This article proposes and implements a lossless compression-decompression algorithm, which enables in parallel compression and decompression of data. By this means, the data transfer requirement of hardware interface and the transmission time of CPU to GPU data transfers are reduced, without sacrificing image quality. In simulation results, the compression ratio reached around 1.7. The encoder design of our lossless compression approach requires low hardware resources and reasonable latency in a field programmable gate array. In addition, the transmission time of transferring data from CPU to GPU with the parallel decoding process improved by threefold, as compared with transferring original uncompressed data. These results show that our proposed lossless compression plus parallel decoder approach not only mitigate the transmission bandwidth requirement to transfer data from hardware front end to software system but also reduce the transmission time for CPU to GPU data transfer.

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Section: Introductionmentioning

confidence: 99%