Abstract: A wavelet coefficient quantization method using a human visual model in an image compression process is provided, which is particularly suitable for remote sensing image compression. A wavelet-domain visual quantization model is obtained through experiments based on human visual characteristics, so as to reflect the relation between distortion of wavelet coefficient blocks and human visual characteristics. The model includes a luminance component, a masking component and a frequency component, where the luminance component is calculated by low frequency coefficients after the kth level wavelet transform, the masking component is calculated by high frequency coefficients of the second and third levels, and the frequency component is calculated by a statistical method. The method may be used in combination with any mainstream wavelet compression method such as EZW, SPIHT or EBCOT.

Abstract: A wavelet coefficient quantization method using a human visual model in an image compression process is provided, which is particularly suitable for remote sensing image compression. A wavelet-domain visual quantization model is obtained through experiments based on human visual characteristics, so as to reflect the relation between distortion of wavelet coefficient blocks and human visual characteristics. The model includes a luminance component, a masking component and a frequency component, where the luminance component is calculated by low frequency coefficients after the kth level wavelet transform, the masking component is calculated by high frequency coefficients of the second and third levels, and the frequency component is calculated by a statistical method. The method may be used in combination with any mainstream wavelet compression method such as EZW, SPIHT or EBCOT.