In this paper, a technique to reduce the overhead of Vector Quantization (VQ) coding is developed here. Our method exploits the inter-index correlation property to reduce the overhead to transmit encoded indices. Discrete Cosine Transform (DCT) is the tool to decorrelate the above correlation to get further bit rate reduction. As we know, the codewords in the codebook that generated from conventional LBG algorithm do not have any specified orders. Hence, the indices for selected codewords to represent respective adjacent blocks are random distributions. However, due to the homogeneous property existing among adjacent regions in original image, we re-arrange the codebook according to our predefined weighting criterion to enable the selected neighboring indices capable of indicating the homogeneous feature as well. Then, DCT is used to compress those VQ encoded indices. Because of the homogeneous characteristics existing among the selected adjacent indices after codebook permutation, DCT can achieve better compression efficiency. However, as we know, DCT introduces distortion by the quantization procedure, which yield error-decoded indices. Therefore, we utilize an index residue compensation method to make up that error decoded indices which have high complexity deviation to reduce those unpleasant visual effects caused by distorted indices. Statistics illustrators and table are addressed to demonstrate the efficient performance of proposed method. Experiments are carried out to Lena and other natural gray images to demonstrate our claims. Simulation results show that our method saves more than 50% bit rate to some images while preserving the same reconstructed image qualities as standard VQ coding scheme.