Vector quantization (VQ) is a famous signal compression method. In VQ encoding, a fast search method for finding the best-matched codeword (winner) is a key issue because it is the time bottleneck for practical applications. To speed up the VQ encoding process, some fast search methods that are based on the concept of multiresolutions by introducing a pyramid data structure have already been proposed in previous works. However, there still exist two serious problems in them. First, they need a lot of extra memories for storing all purposely constructed intermediate levels in a pyramid, which becomes an overhead of memory. Second, they completely discard the obtained Euclidean distance that has already been computed at an intermediate level whenever a rejection test fails at this level during a search process, which becomes an overhead of computation. In order to solve the overhead problems of both memory and computation as described above, this paper proposes a memory-efficient storing for vector and recursive computation for Euclidean distance level by level based on a 2-pixel-merging (2-PM) sum pyramid, which can thoroughly reuse the obtained value of Euclidean distance at any level to compute the next rejection test condition at a successive level. Mathematically, this method does not need any extra memories at all and can reduce the original computational burden that is needed in conventional nonrecursive computation to about half at each level. Experimental results confirm that the proposed method outperforms the previous works.