Description: The optimal predictors of a lifting scheme in the general n-dimensional case are obtained and applied
for the lossless compression of still images using rst quincunx sampling and then simple row-column
sampling. In each case, the e ciency of the linear predictors is enhanced nonlinearly. Directional post-
processing is used in the quincunx case, and adaptive-length postprocessing in the row-column case. Both
methods are seen to perform well. The resulting nonlinear interpolation schemes achieve extremely e cient
image decorrelation. We further investigate context modeling and adaptive arithmetic coding of wavelet
coe cients in a lossless compression framework. Special attention is given to the modeling contexts and
the adaptation of the arithmetic coder to the actual data. Experimental evaluation shows that the best of
the resulting coders produces better results than other known algorithms for multiresolution-based lossless
image coding.
- The optimal predictors of a lifting scheme in the general n-dimensional case are obtained and applied
for the lossless compression of still images using rst quincunx sampling and then simple row-column
sampling. In each case, the e ciency of the linear predictors is enhanced nonlinearly. Directional post-
processing is used in the quincunx case, and adaptive-length postprocessing in the row-column case. Both
methods are seen to perform well. The resulting nonlinear interpolation schemes achieve extremely e cient
image decorrelation. We further investigate context modeling and adaptive arithmetic coding of wavelet
coe cients in a lossless compression framework. Special attention is given to the modeling contexts and
the adaptation of the arithmetic coder to the actual data. Experimental evaluation shows that the best of
the resulting coders produces better results than other known algorithms for multiresolution-based lossless
image coding.
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Author:dee |
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Description: 图像融合论文，基于提升小波变换与自适应PCNN的医学图像融合方法-Image fusion papers, medical image fusion method based on lifting wavelet transform and adaptive PCNN' s Platform: |
Size: 431104 |
Author:冯晓洁 |
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