在以離散小波轉換 (discrete wavelet transform，DWT)為基礎的壓縮技術中，SPECK (set partitioning embedded block)是一個既快速又有效率的靜態影像壓縮演算法。在本篇論文當中，我們從SPECK延伸出一套新的壓縮演算法PSPECK (prepartition SPECK)。為了改善影像的PSNR (peak signal-to-noise ratio)值和減少位元率，我們藉由不同次頻帶 (subband)之間的相關性來預測位於LIS (list of insignificant sets)中集合的重要性。而且本篇論文所提出來的方法，可以和其他以四分樹 (quadtree)為基礎的編法方法做結合。實驗結果顯示，在壓縮過後的影像品質上PSEPCK表現比SPECK優異，尤其在高位元率的情況下更為明顯。

The set partitioning embedded block (SPECK) algorithm is a fast and efficient technique for still image compression. In this thesis, we propose a novel wavelet-based coding scheme, called prepartition SPECK (PSPECK), on the extension of SPECK. In order to improve the peak signal-to-noise ratio (PSNR) performance, we predict the significance of each set in the list of insignificant sets (LIS) by exploiting inter-subband correlation for reducing the bit budget. Furthermore, the proposed scheme can be combined with other quadtree-based coding techniques. Experimental results show that the proposed method outperforms SPECK, especially at high bit rates.

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