隨著多媒體資訊的快速成長，視訊編碼標準採納了許多新技術來增進影像畫質與壓縮效率，然而也會大幅地增加龐大的計算複雜度。本論文提出三個重要的議題進行探討，首先提出一個影像放大法和客觀的影像放大評估方法。再從理論、分析與實作等不同面來探討最具有代表性的運動估測演算法和所提出的快速運動估測演算法。最後，提出一個有效地應用正負號做相差值之集中的影像壓縮法。
影像放大為數位影像處理領域中的一個重要技術，傳統的影像放大技術所遇到的主要問題是影像在放大之後，會使得影像中的物體邊緣有鋸齒狀、模糊等失真問題，因而造成影像品質下降。因此我們提出改良的離散餘弦轉換來獲得運算快速且高品質的超大尺度之放大影像。提出的影像放大法是以矩陣相乘實作離散餘弦轉換來加速運算時間，在超大尺度放大時相對快速，同時也解決了傳統離散餘弦轉換放大時影像偏移與方塊效應的問題。並且提出的影像放大法可以藉由調整轉換矩陣的維度大小來達成指定像素尺寸之放大，並考慮到全域之細節，處理整張影像以獲得更佳品質的放大影像。實驗結果證實提出的改良式離散餘弦轉換放大法有效地達到快速且高品質之影像放大。除此之外，眾多文獻中因為特定縮小方法差異之影響，往往無法客觀地驗證放大影像之品質。因此，此論文基於基準函數的峰值信噪比方法，以基準函數所生成的虛擬圖形作為實驗資料，提出一個客觀的嶄新影像放大評估法。
其次在運動估測部分，運動估測能去除視訊訊號中在時間軸上的冗餘性，因此是許多高性能視訊編碼標準常見的關鍵技術之一。但若是處理的視訊影像之解析度不是很高時，參考資訊實屬有限，若能將提出的影像放大法應用在進行運動估測之運作之前，將能提供更充足的運動估測之資訊，因此，我們基於影像放大法為前處理，提出一個有效的區塊比對運動估測演算法。提出的演算法包含三個步驟: 一、基於提出的影像放大法將原本影像放大N倍後，以四張跳躍的畫面為一群組來建構一個多項式插補模型，並採用全搜尋法來決定最理想的全域運動向量，以獲得初始的搜尋點。二、在插補步驟中，將第一步驟得到的初始搜尋點之運動向量縮小N倍後，根據其預測的方向性執行一個適應性的搜尋區塊尺寸決策模式來快速得到最佳的運動向量。三、使用中途停止運算技術來減少計算量。實驗結果也顯示提出來的演算法與全搜尋演算法比較，執行速度最快能提升366.47倍，並且在PSNR效能上只有降低0.52%的而已。
最後，針對靜態影像編碼進行討論。在近年來，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，以期望能產生壓縮率高的預測圖。許多研究著重在加強預測的準確性，為了尋找鄰近點的最佳係數，卻增加了時間複雜度。本研究的目的，在於尋找有效且快速的方法，並在不增加時間複雜度之情況下加強壓縮率。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。此壓縮方法的貢獻是以一種嶄新的方式集中相差值進而提升壓縮率。實驗結果顯示在細節較多或是相同紋理較少的影像，我們所提出的方法比CALIC的壓縮率還高。

With the rapid growth of multimedia information, video coding standards utilize several new techniques to improve image quality and compression ratio. However, it increases huge computational complexity. This dissertation studies three topics. First, a fast large-scale image enlargement method with an objective evaluation approach is proposed. Secondly, an efficient block-matching algorithm is proposed for motion estimation from the aspects of theory, analysis and experiment. Finally, we present an improved lossless image compression with efficient error value centralization by sign bits.
In the first topic, image enlargement is a critical technique in image processing domain, but there are the shifting effect and the blocky effect that occur in traditional DCT for image enlargement. Therefore, a fast large-scale image enlargement method via the improved discrete cosine transform (DCT) is proposed to improve the computational speed and quality of the large-scale enlargement image. The proposed image enlargement algorithm based on DCT saves computational time by multiplication of the DCT matrix, especially for the large-scale image enlargement. Compared to the traditional DCT approach, the improved approach eliminates the image shifting and blocky effects. In comparisons to other interpolation methods, the proposed DCT enlargement outperforms other interpolation methods in edge details because the proposed DCT enlargement considers the global frequency information of the whole image. With the DCT enlargement, it is easy to implement the arbitrary pixel-size-based zooming of an image by employing the suitable size of transform matrix. Illustrative examples show the proposed approach performs higher image quality and lower computation time than other traditional and existing methods. On the other hand, traditional researches use the specific image reduction methods which significantly influence the comparison of enlargement algorithm. And, specific image reduction methods will lead unfair measure of enlargement performance. Therefore, an objective novel evaluation approach, implemented by the benchmark function-based peak signal-to-noise ratio (PSNR), particularly suitable for evaluating the performance of a large-scale enlargement of a small size image is proposed in this dissertation.
The second topic is motion estimation, for reducing the temporal redundancy, the development of a fast and effective ME algorithm has been a challenging task for years. When the resolution of video sequences is not high/clear, the reconstructive performance through motion estimation will be limitative. The proposed image enlargement method can provide more sufficient information for motion estimation. Therefore, we proposed an effective block-matching motion estimation algorithm based on image enlargement preprocessing. The proposed algorithm consists of three effective steps: 1) apply the full search algorithm to construct a polynomial interpolation (PI) model from the group with four skipping frames to determine near-optimal global motion vector for the initial search point, 2) perform an adaptive search block-size decision in the interpolation step to refine the motion vector (MV) and 3) use the half-way stop technique to reduce search points. Our experimental results show that the proposed algorithm achieves a maximum speed-up factor of 366.47 with only 0.52% peak signal-to-noise ratio (PSNR) degradation in comparison with the full search algorithm.
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. Here, we present an improved lossless image compression based on the prediction method with the proposed efficient error value centralization by sign bits. The contribution of this dissertation 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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