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 highly visually significant to human vision system. Based on the spatial model, we apply fuzzy logic theory to detect visual significant edge points and based on these edge points to construct a Visual Perception Sensitive Map (VPSM) for wavelet coefficient thresholding scheme. Only the visual significant coefficients are retained and the rest are discard. This approach can achieve a high image compression ratio while minimizing the visual quality distortion of the reconstructed image. In addition, we develop an adaptive quantization scheme for the wavelet coefficients at each of the subbands. This quantization scheme is developed based on the HVS frequency model to minimize the visual errors caused by the quantization. In our image compression system, both the 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 with low visual distortion coder is obtained.