The discrete wavelet transform performs multiresolution analysis, which effectively decomposes a digital image into components with different degrees of details. In practice, it is usually implemented in the form of filter banks. If the filter banks are cascaded and both the low-pass and the high-pass components are further decomposed, a wavelet packet is obtained. The coefficients of the wavelet packet effectively represent subimages in different resolution levels. In the energy-sorted wavelet- packet decomposition, all subimages in the packet are then sorted according to their energies. The most important subimages, as measured by the energy, are preserved and coded. By investigating the histogram of each subimage, it is found that the pixel values are well modelled by the Laplacian distribution. Therefore, the Laplacian quantization is applied to quantized the subimages. Experimental results show that the image coding scheme based on wavelet packets achieves high compression ratio while preserving satisfactory image quality.