在最近二十年間，文獻相繼提出基於預測方法的無失真影像壓縮演算法，根據空間上的相關性，使用不同的係數加權於鄰近點上，以期望能產生與原圖相差甚小的預測圖。許多研究皆著重在加強預測的精準性，為了尋找鄰近點的最佳係數，卻增加了相對的時間複雜度。本論文的研究目的，在於尋找有效且快速的方法，並在不增加多餘時間複雜度的情況下加強壓縮率。許多的研究理論多是期望能加強預測的精準性，卻花費了大量的時間在尋找最佳的鄰近點。在本論文改進了基於預測方法之無失真影像壓縮法: 有效地應用正負號做相差值之集中。本方法的貢獻是以一種嶄新的方式集中相差值來提升壓縮率。實驗結果顯示在細節較多或是相同紋理較少的圖片，我們所提出的方法比CALIC的壓縮率還高。

In the last two decades, there exist many high-performance prediction-based methods that use different coefficients of causal neighbors in order to exploit the relationship of spatial energy to produce a less error image. Besides, more and more researches focus on the accuracy of predictor; nevertheless, the predictor spends a lot of time on finding the best coefficients of causal neighbors. The objective of our research is to propose an efficient and implementable method to improve compression ratio, without increasing extra computation complexity. In this thesis, we present an improved lossless image compression based on the prediction method, by the proposed application of efficient error value centralization by sign bits. The contribution of this thesis is to centralize error values in a novel way to improves coding performance. Experimental results show that our proposed method achieves higher compression ratio than the context-based, adaptive, and lossless image codec (CALIC) method for the images with many details or slightly regular texture.

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