本論文提出一個植基於空間資料結構和小波轉換的灰階影像壓縮方法。首先依據使用者設定的灰階容忍誤差值，利用S樹表示法(S-tree spatial data structure)將灰階影像切割成區塊子影像，再針對S樹所分割的區塊子影像，我們將其分成兩種型態，分別為同質(homogeneous)區塊與異質(nonhomogeneous)區塊。同質區塊為低頻平滑區塊影像，而異質區塊代表高頻紋理複雜區塊影像，針對異質區塊影像，本論文利用小波轉換(Wavelet transform)和EBCOT編碼演算法進行區塊影像壓縮，來大幅提升灰階影像的壓縮率和品質，本論文所提出方法顯示與先前所提出方法相較之下，可提升約60%平均壓縮率，此外在影像品質方面也提升約4db的PSNR值。

A gray image representation using spatial and DWT-based (SDWT-based) approach is presented. Our proposed SDWT-based gray image representation consists of the linear-tree table, the leaf-type table, and the color table.The leaf-type table is used to discriminate between the leaf nodes encoded by S-tree coding (STC-based) approach for homogenous blocks and the leaf nodes encoded by DWT-based for nonhomogeneous blocks.In each nonhomogeneous block is encoded by DWT with EBCOT algorithm. Based on some popular gray images,experimental results show that our proposed gray image representation over the previously published SDCT-based approach has about 61% compression improvement ratio in average. Our proposed has better image quality over 4 db of average PSNR when compared to the previous SDCT-based image representation.

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