In this paper, we propose to incorporate both spatial and frequency models of HVS into wavelet transform image coding. The process of wavelet transform decomposition, which splits the spatial frequency domain to several octave bands by dilation and translation of a single basic wavelet, is similar to that of frequency model HVS. Moreover, according to spatial model of HVS, some compact physical features like contours and regions are with high visual perceptive significance to human vision system. Based on the spatial model we develop a visual perception sensitive map and use the map to develop a wavelet thresholding scheme in order to achieve a high image compression ratio while retaining a high visual quality of the reconstructed image.After removing the less visually significant coefficients, we developed an adaptive quantization scheme for transformed coefficients at each of the subbands. This quantization scheme is developed based on the HVS frequency model to minimize the visual error due to quantization. In our image compression system, both frequency and spatial aspects of HVS to the image have been taken into consideration. We preserve the highly visual perceptive wavelet coefficients and minimize the visual distortion of coefficients in each of the decomposed band. As a result, a high compression ratio and low visual distortion coder is obtained.