Vector quantization (VQ) is a popular image compression method and the encoding speed of VQ is very important to its practical applications. In a conventional encoding process of VQ, because a lot of k-dimensional (k-D) Euclidean distances must be computed so as to find out the best-match for each input vector, VQ is computationally very expensive. In order to avoid immediately computing the real Euclidean distance for a candidate codeword, IEENNS method has been proposed to reject the unlikely codeword by using the famous scalar features of the sum and the variance of a k-D vector. Furthermore, in order to improve the precision of Euclidean distance estimation so as to enhance the rejection capability, by dividing a k-D vector in half to generate two (k/2)-D subvectors and then apply IEENNS method again to each of the subvectors, a complete-version C-SIEENNS method and a simplified-version S-SIEENNS method have been reported recently as well. Apparently, how to construct the two (k/2)-D subvectors is the core problem in a subvector-based method for achieving a higher encoding performance. However, the previous works just fixedly construct their two subvectors by using the first half original vector of [1 ∼ k/2] dimensions and the second half original vector of [k/2+1 ∼ k] dimensions for simplicity. It is clear there is no guarantee that this kind of subvector construction way is optimal. Instead, this paper proposes a criterion to construct two better subvectors by letting the difference between the two partial sums approach the maximum based on adaptively analyzing the property of each codeword offline. Experimental results confirmed that by simply replacing the fixed subvectors with the adaptively constructed subvectors in S-SIEENNS method, it can further improve the search efficiency by 19.9%∼36.8%.