The encoding of vector quantization (VQ) needs expensive computation for searching the closet codeword to the input vectors. To reduce the computational burden, many researchers have developed some efficient full-search-equivalent algorithms by using the characteristics of the mean values and variances of an input vector to reject unlikely codewords. However, some computational redundancies still exist. As far as we know, Pan's method has the best performance of rejecting unlikely codewords using both characteristics. In this work, we introduce a technique to efficiently partition a vector into two dynamic subvectors according to the patterns inside the block (or vector), and then further improve the computational load given by Pan's method. Experimental results show that the proposed method is superior to other algorithms in terms of processing time and the number of distortion calculations. Compared with the best encoding algorithm, our algorithm can further reduce the processing time and the number of distortion calculations for various codebook sizes by 14.7 to 41.3% and 14.9 to 44.6%, respectively.