網路電視(IPTV)是目前最熱門的互動式電視系統，為了降低網路電視系統之成本，我們設計一個高效能的H.264無失真(H.264 Lossless，簡稱H.264-LS)影像壓縮方法，以降低平台系統之硬體。傳統的H.264-LS單純使用內框預測(Intra Prediction)和高效率熵編碼，仍無法比JPEG-LS和JPEG2000有更佳的壓縮率。本論文提出影像變化內容的之適應性熵編碼法(Adaptive Entropy Coding，簡稱AEC)與適應性二值算數編碼法(Adaptive Binary Arithmetic Coding，簡稱ABAC)以增加H.264-LS壓縮率。我們透過內框預測殘值以取得影像變化內容(Image Variation Content，簡稱ICV)的資訊去適應性的調整熵編碼法；利用近似H.264無失真(Near H.264-LS)影像壓縮演算法與ABAC去獲得高性能的H.264-LS。模擬結果顯示我們提出之改良的H.264-LS優於原始的H.264-LS約24.8%壓縮率並且優於過去傳統的無失真影像壓縮技術。

Internet Protocol Television (IPTV) is now the hottest interaction internet TV system in the world. In order to reduce the cost of IPTV set, we design a efficient H.264 lossless compression method to reduce its hardware. Traditional H.264 base on intra prediction and efficient entropy coding is not able to provide a better compression ratio than JPEG-LS and JPEG 2000. In this thesis, we suggest adaptive entropy coding (AEC)and adaptive binary arithmetic coding(ABAC) to improve the compression ratio of H.264-LS, where the image variation content (ICV) can be easily obtained from the intra prediction residues. For adjusting adaptive the entropy coding, we can use the New H.264-LS compression algorithm and adaptive binary arithmetic coding (ABAC) to obtain efficient H.264-LS. The simulation results show that the proposed H.264-LS can achieve more 24.8% compression ratio than original H.264-LS and have better video compression technique than traditional lossless compression method.

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