The combination of singular value decomposition (SVD) and vector quantization (VQ) is proposed as a compression technique to achieve low bit rate and high quality image coding. Given a codebook consisting of singular vectors, two algorithms, which find the best-fit candidates without involving the complicated SVD computation, are described. Simulation results show that the proposed methods are better than the discrete cosine transform (DCT) in terms of energy compaction, data rate, image quality, and decoding complexity.