近十幾年來，離散小波轉換已被廣泛地應用在各個研究領域，包 括信號分析、影像壓縮、視訊壓縮、圖形辨識以及數值分析等等。由於離散小波轉換具有極佳能量集中的特質和與生俱來多重解析的特性，使它在影像及視訊壓縮編碼系統中受到極高的重視。傳統離散小波轉換是以濾波器為主，然而其運算複雜度非常龐大，因此現今大都使用提升式架構來降低運算複雜度，而且又易於實現，不過對於二維提升式離散小波轉換而言，除了運算複雜度之外，還有著硬體成本上的問題－龐大內部的記憶體。 因此，在本論文中，我們提出了二維提升式離散小波轉換之有效 記憶體架構應用於Motion-JPEG2000，此架構包括一維的行處理器、 內部記憶體與一維的列處理器，此架構不僅支援無失真與失真的兩種模式，而且處理的速度相當快，最主要的優點是大大地減少內部記憶體的容量。例如，以一張N x N 的影像而言，如果要進行一階的二維的離散小波轉換，對於5/3 濾波器只需要2N 記憶體的容量，而9/7濾波器也只需要4N 記憶體的容量。 與其它同為二維提升式離散小波轉換架構的比較之下，我們所提 出的架構對於改善記憶體的貢獻是相當出色的，而且硬體架構的實現 並不難，也可支援其它即時的應用裝置，不管是影像還是視訊上。 In the last few years, discrete wavelet transform (DWT) has been used for a wide range of applications including image coding and compression, speech analysis, pattern recognition, and computer vision. DWT can be viewed as a multi-resolution decomposition of a signal. This means that it decomposes a signal into several components in different frequency bands. It always needs a large amount of computations and memory to perform the DWT. In order to achieve the real-time processing, reducing of memory and computational complexity and increasing the efficient hardware utilization are necessary. Therefore, we propose a memory-efficient architecture of lifting based two-dimensional discrete wavelet transform (2-D DWT) for motion-JPEG2000. The proposed 2-D DWT architecture consists of a 1-D row processor, internal memory, and a 1-D column processor. The main advantage of this 2-D DWT is to reduce the internal memory requirement significantly. For an N×N image, only 2N and 4N sizes of internal memory are required for the 5/3 and 9/7 filters, respectively, to perform the one-level 2-D DWT decomposition. Moreover, it supports both lossless and lossy operation for 5/3 and 9/7 filters with high operation speed. The proposed 2-D DWT surpasses the existed lifting-based designs in the aspects of low internal memory requirement. It is suitable for VLSI implementation and can support various real-time image/video applications such as JPEG2000, motion-JPEG2000, MPEG-4 still texture object decoding, and wavelet-based scalable video coding.
