In this paper, we propose a novel lossless data hiding method for vector quantization (VQ) compressed images. The existing methods sort the codebook based on the intensity of codewords to obtain rearranged indices (RI). The indices in RI are predicted, and the secret bits, indicators, and prediction errors are then encoded to construct an output code stream (CS). However, the neighboring indices of RI might not have similar intensity, which could impede the coding efficiency. Moreover, the equal-length indicators used in the existing methods might not efficiently represent the cases categorized in the encoding stage. As a result, the size of CS in these methods is likely to increase unnecessarily. The proposed method rearranges the indices to make their spatial correlations stronger, so that the obtained RI is more advantageous for predictive coding. A weight-controlled least square estimator is employed for prediction, and the indicators are better assigned according to the occurrence frequencies of prediction errors. The run-length coding technique is adopted to further reduce the code length. Experimental results show that the proposed method effectively reduces the bitrate for various codebook sizes.