由於電腦網路的發展，影像資料時常經由網路傳輸，因為影像資料太過龐大，故有效的壓縮成為重要的課題，如此可節省傳輸時間及網路頻寬。向量量化是很有效的影像壓縮演算法，其最大的特點在簡單的解碼器及即時的解壓縮，尤其適合一個編碼器對多個解碼器的應用環境。
在本論中首先回顧幾種主要的向量量化演算法，並且提出一個可以改良樹狀結構向量量化演算法的方法，應用此方法亦可同時安排碼本中碼向量的順序，排列以後的碼本若用PDS演算法來編碼，可加速其編碼所需之時間。

我們在DCT-TSVQ及PCA-TSVQ上加入我們的方法，實驗結果顯示應用新的方法確實可以提高壓縮後的影像品質，效果接近LBG演算法，只需多一點碼本生成的時間，也大大加速了編碼所需的時間，大約需原演算法的40%左右，總合時間也少於原演算法所需。此實驗是在Celeron 400配備128MB記憶體的個人電腦執行，改良後的DCT-TSVQ需要大約1.5秒，而改良後的PCA-TSVQ需要約3秒壓縮一張512×512的灰階256色影像。壓縮比為0.625bpp，且得要令人滿意的影像品質。在未來更快速的電腦上，將需更少的時間。

Vector Quantization (VQ) is an efficient technique for image compression. A new fast VQ scheme, called DCT-VQ, based on a two-dimensional discrete cosine transform (2-D DCT) is presented. The significant features of training images are extracted by using the 2-D DCT. A codebook is generated by partitioning the training set into a binary tree. Each training vector at a nonterminal node of the binary tree is distributed to one of the two descendants by comparing a single feature associated with that node to a threshold. A modified scheme based on DCT-VQ is proposed. It partitions the training data into a binary tree according to the distribution of training vectors. Experimental results show that the new scheme results in a better codebook

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