A novel quality scalable image coding scheme at low-medium based on non-redundancy contourlet transform is proposed. Using the bi-orthogonal wavelet decomposition and directional filter banks, the image non-redundancy sparse representation can be obtained, in which not only it is capable of efficiently characterizing the linear singularity and texture in images but also it can overcome the drawback of 4/3 redundancy in classical contourlet transform. According to the statistical distribution characteristic of coefficients of each subband of non-redundancy contourlet, an elaborated reposition algorithm for the transform coefficients is presented to construct the spatial-orientation tree. Furthermore, the “zero-tree” characteristic of this structure is statistically validated. Then under the principle of set partitioning in hierarchical tree-(SPIHT) algorithm and threshold quantization method, the quality scalable image coding procedure is obtained. Our experimental results demonstrate that at the low to medium rate, the decoding image of the proposed algorithm is competitive to the JPEG2000 in terms of PSNR value, but is visually superior to the wavelet based SPIHT or JPEG2000, especially for those regions with plentiful textures and edge.