Context modeling based lossless compression of radio-frequency data for software-based ultrasound beamforming

Lim, C.H.; Kim, Gi-Duck; Yoon, Changhan; Kim, Jung-jun; Kim, Kang-sik; Song, Tai-Kyong; Yoo, Yangmo

doi:10.1016/j.bspc.2013.06.010

Cited by 2 publications

(1 citation statement)

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“…Spatial redundancy can be removed by prediction. Similarly, statistical redundancy can be removed by entropy coding the residuals [20]. Entropy coding allots fewer bits to higher frequency symbols and more bits to lower frequency symbols.…”

Section: Entropy Codingmentioning

confidence: 99%

Novel Near-Lossless Compression Algorithm for Medical Sequence Images with Adaptive Block-Based Spatial Prediction

et al. 2016

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To address the low compression efficiency of lossless compression and the low image quality of general near-lossless compression, a novel near-lossless compression algorithm based on adaptive spatial prediction is proposed for medical sequence images for possible diagnostic use in this paper. The proposed method employs adaptive block size-based spatial prediction to predict blocks directly in the spatial domain and Lossless Hadamard Transform before quantization to improve the quality of reconstructed images. The block-based prediction breaks the pixel neighborhood constraint and takes full advantage of the local spatial correlations found in medical images. The adaptive block size guarantees a more rational division of images and the improved use of the local structure. The results indicate that the proposed algorithm can efficiently compress medical images and produces a better peak signal-to-noise ratio (PSNR) under the same pre-defined distortion than other near-lossless methods.

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Section: Entropy Codingmentioning

confidence: 99%