隨著多媒體技術快速的發展以及電腦技術的進步，現今有越來越多的多媒體資料儲存在影像資料庫中或是在網路上散播著。如何將這些龐大的多媒體資料以有效的壓縮方式儲存，並且有效的管理與迅速的查詢，早已是各方學者研究的重要課題。
近幾十年來，有許多學者提出向量量化技術於影像檢索。本論文提出分類向量量化方法結合四元樹分割技術(QCVQ)將影像分割成大小不同之區塊，並加以分類，最後，建立影像索引以進行檢索。
針對傳統做法使用VQ法對於影像資料庫編碼簿的訓練以及影像特徵擷取時需耗費大量時間的缺點，本研究提出一個有效的改善方法，藉由考慮每個區塊的性質(如邊緣，一致性)，產生多個子編碼簿，有效地描述影像內容，提升了影像檢索在精確率與速度上的整體效能。

With the rapid development of multimedia and the advancement of computer technique, nowadays more and more multimedia information have been stored in image databases, and spread in internet. It has been an important issue for many researchers to find ways to achieve effective compression in order to both store multimedia information and provide effective administration and quick retrieval.
In the past several decades, VQ based image retrieval has been proposed by many researchers. In this study, we propose a classified vector quantization method combined with quadtree segmentation technique to get a set of square regions that vary in size and then classify these partitioned image blocks. Finally, we process image indexing to image retrieval。
Due to the cost of spending much time in codebook training of image database and image indexing, our scheme considers attributes of all blocks such as edge and homogeneity, and generate many subcodebooks that efficiently describe image content, to improve the total performance in both precision and speed.

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