Vector quantization (VQ) is an effective image coding technique at low bit rate. Side-match finite-state vector quantizer (SMVQ) exploits the correlations between the neighboring blocks (vectors) to avoid large gray level transition across block boundaries. In this paper, a new adaptive quadtree-based side-match finite-state vector quantizer (QBSMVQ) has been proposed. In QBSMVQ, the blocks are classified into two main classes, edge blocks and nonedge blocks, to avoid selecting a wrong state codebook for an input block. In order to improve the image quality, edge vectors are reclassified into sixteen classes. Each class uses a master codebook that is different from the codebook of other classes. In our experiments, results are given and comparisons are made between the new scheme and ordinary SMVQ coding techniques. As will be shown, the improvement of QBSMVQ over the ordinary SMVQ is up to 3.13 dB at nearly the same bit rate. Moreover, the improvement over the ordinary VQ can be up to 4.30 dB at the same bit rate for the image Lena. Further, block boundaries and edge degradation are less visible because of edge-vector classification. Hence, the perceived improvement in quality over ordinary SMVQ is even greater for human sight.