Novel Near-Lossless Compression Algorithm for Medical Sequence Images with Adaptive Block-Based Spatial Prediction.
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 pr...
| Publicado en: | Journal of Digital Imaging Vol. 29; no. 6; pp. 706 - 716 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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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