文件名称:1-s2.0-S0030402613001447-main
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The wavelet transform divides image into different but interrelated multi-resolution and multi-level
sub-bands, so quantization coding strategy of image wavelet coefficient has great flexibility. To address
to energy aggregation and correlated features of image wavelet coefficient, Hilbert and singular value
truncating were introduced to wavelet to propose an Improved Wavelet Lossless Compression Algo-rithm (IWLCA). It mainly performs classification rearrangement on low-frequency sub-band coefficient
of wavelet image in accordance with Hilbert curve, but conducts singular value truncating transform
on high-frequency coefficient.-The wavelet transform divides image into different but interrelated multi-resolution and multi-level
sub-bands, so quantization coding strategy of image wavelet coefficient has great flexibility. To address
to energy aggregation and correlated features of image wavelet coefficient, Hilbert and singular value
truncating were introduced to wavelet to propose an Improved Wavelet Lossless Compression Algo-rithm (IWLCA). It mainly performs classification rearrangement on low-frequency sub-band coefficient
of wavelet image in accordance with Hilbert curve, but conducts singular value truncating transform
on high-frequency coefficient.
sub-bands, so quantization coding strategy of image wavelet coefficient has great flexibility. To address
to energy aggregation and correlated features of image wavelet coefficient, Hilbert and singular value
truncating were introduced to wavelet to propose an Improved Wavelet Lossless Compression Algo-rithm (IWLCA). It mainly performs classification rearrangement on low-frequency sub-band coefficient
of wavelet image in accordance with Hilbert curve, but conducts singular value truncating transform
on high-frequency coefficient.-The wavelet transform divides image into different but interrelated multi-resolution and multi-level
sub-bands, so quantization coding strategy of image wavelet coefficient has great flexibility. To address
to energy aggregation and correlated features of image wavelet coefficient, Hilbert and singular value
truncating were introduced to wavelet to propose an Improved Wavelet Lossless Compression Algo-rithm (IWLCA). It mainly performs classification rearrangement on low-frequency sub-band coefficient
of wavelet image in accordance with Hilbert curve, but conducts singular value truncating transform
on high-frequency coefficient.
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1-s2.0-S0030402613001447-main.pdf