近年來網際網路結合數位內容產業蓬勃發展，手持行動裝置的普及以及多媒體的壓縮技術也不斷突破創新，因此數位化之影音資料量也呈現指數型成長，延伸出快速複製與散播傳輸的問題進而侵害私人財產權的專屬性，這已成數位著作權中最重要的一項議題，有別於數位浮水印在本篇研究著眼以內容為基礎之副本偵測的偵測方式. 則無需事先在影像或影片中嵌入任何標記等資訊；此外，現今數位化資料呈現爆炸性成長，但其中仍有不少資料重複但只是畫質、大小或者因壓縮格式的不同而有所差異，為了避免浪費資料庫空間以及檢索之時間進而預先過濾、篩選資料以達到有效率的資料庫管理。
本論文針對內容檢索，利用HEVC於壓縮域上的特徵進行抽取，以畫面內預測（intra prediction）所取得之不同預測模式以及其殘餘值（residual）作為特徵。實驗結果顯示，所提出之方法可大幅減少檢索時所需使用的大量特徵空間，且檢索效能精準率及查全率無論是在不同量化參數之下或者不同畫面大小的情況分數皆可達到80以上的效能。

關鍵字：壓縮域、副本偵測、畫面內預測、HEVC

The rapid growth of internet and online video data bring the difficulties not only to database management but also to digital right management. In the past decade, numerous algorithms were developed for video copy detection.
Because almost all digital video data exist in a compression format, many copy detection techniques operated in compression domain were proposed. However, these methods did not perform well with the sequences that had differentquantization parameter (QP).
This thesis focuses on the content-based copy detection in compression domain, especially with the capability resisting multi-QP and frame sizes. The extracted features are based on the I-frame coding information in HEVC that is the newer coding standard. We propose to employ the direction mode and residual coefficients as the texture feature.
The experimental results show that the proposed method can reduce the resource consumption and archive the similar performance with the pixel domain approach.

Keywords : Compression domain, Content-based copy detection, Intra Prediction, HEVC

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