In recent years, principal component analysis (PCA) has attracted great attention in image compression field. However due to the linear nature, PCA cannot simultaneously explain the global and local characteristics of the input image. To achieve high compression rate, only a few basis vectors should be used. The fewer the basis vectors used, the more local information will be lost. To solve this problem, a number of improved PCA approaches have been proposed. The basic idea is to reduce the error by using different basis vectors for different sub-spaces of the problem space. These algorithms are non-linear, but very time-consuming and cannot be used easily. In this paper, a VQ based mixture of principle components (MPCs) is proposed. Experimental results show that the proposed approach, although simpler, is actually better than existing PCA based approaches.