本論文所提出的演算法是對影像做無失真和近無失真的壓縮，在無失真壓縮部分，是以DPCM為基礎及提出的無失真壓縮演算法為核心。而近無失真壓縮的部分，則是依循無失真壓縮的作法再加上適應性量化來達到目標。提出的無失真演算法是針對影像中邊(edge)的特性分成5種模式來做預測，其中包含規律模式(regular mode)、水平邊緣模式(horizontal edge mode)、垂直邊緣模式(horizontal edge mode)、對角邊緣模式(diagonal edge mode)和無邊緣模式(non-edge mode)。模式的預測是利用相鄰像素的特性來判斷。此外，在適應性量化的設計上是考慮人眼對平滑區域的失真比複雜區域的失真敏感，所以針對這點，在量化部分使用了3個量化步階(Quantization step size)。實驗結果顯示，在無失真壓縮部分，雖然壓縮比並不如JPEG-LS，但計算複雜度和空間複雜度上皆比JPEG-LS低，且在近無失真壓縮部分，在視覺效果上亦比JPEG-LS的近無失真結果佳。

　　This thesis is aim at lossless and near lossless image compression. The lossless compression part is based on DPCM, and it is the core of the near-lossless part. In the near lossless part, the adaptive quantization is used. The proposed lossless algorithm is designed in five modes which are the regular mode, the horizontal edge mode, the vertical edge mode, the diagonal edge mode and the non-edge mode. The mode decision is also needed to decide which mode is given by observing the behavior of the pixels around the predictive pixel. In terms of HVS, human eyes are more sensitive to smooth regions, that is, serious distortions existing in smooth regions is easier to see. Owing to this reason, in quantization part, three quantization step sizes are designed. Experimental results show that, in lossless part, although the compression ratio of the proposed algorithm is not as good as JPEG-LS, but the computational complexity is decreased by discarding the context modeling. In near-lossless part, the perceptual quality of the reconstructed images is also better than JPEG-LS.

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