由於紋理模型技術可將大量的紋理資訊濃縮為少量的模型參數，因此在靜態數位影像壓縮領域成為突破壓縮比瓶頸的關鍵技術。目前紋理模型技術所遭遇到的主要問題在於：1. 缺乏對紋理模型的基礎研究以致於相關技術可靠度不足；2. 沒有將紋理模型應用於適當的資訊平面，以致於無法有效的提升影像品質。
本論文首先對選用的紋理模型—自回歸模型(auto-regressive model, AR model)進行相關的基礎研究。由於AR model所重建出的訊號可視為差分方程式的解，因此本論文從求解差分方程式的基礎理論出發，分析AR model階數對重建訊號周期性的影響，以及初始值對重建訊號量值範圍、周期性與方向性的影響。並根據研究結果提出階數選擇方法以及大量壓縮初始值之方法。
最後，本論文整合出一套完整的靜態數位紋理影像壓縮技術，其策略是僅對輸入影像的高頻之低位元資訊使用紋理模型。從實驗結果可以證明，本論文提出的紋理影像壓縮技術僅需額外付出極低的資料量(0.05 bits/pixel)，便可相當有效的提升高壓縮比下的紋理影像品質。

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