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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/79554


    Title: 利用支持向量機降低HEVC畫面間預測運算複雜度之研究;Reduction of Computational Complexity for HEVC Inter Prediction with Support Vector Machine
    Authors: 劉家凱;Liu, Jia-Kai
    Contributors: 通訊工程學系
    Keywords: HEVC;支持向量機;畫面間預測;移動向量;RDO
    Date: 2019-01-25
    Issue Date: 2019-04-02 15:00:57 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 近年來隨著技術進步以及人類需求,高解析度的影像產品也越來越多,而為了能夠有效壓縮伴隨著高解析度而來的龐大資料量,HEVC使用許多的方法來有效的降低位元率。因此本論文提出應用SVM於編碼單元深度的分類以及預測單元模式的分類。編碼單元以畫面間預測的移動向量值的資訊、合併模式的CBF、鄰近區塊深度資訊做為特徵(Feature)將一個CTU分類成只做深度0、深度0~1、深度0~2、深度0~3四種類別,以此略過特定深度的運算。預測單元以畫面間預測的移動向量值的資訊、Skip flag、鄰近區塊RDO資訊做為特徵(Feature),判斷預測單元做完Inter2N×2N後是否需要提前中止,進而節省掉後續預測模式所需花費的運算時間。最後結合兩種演算法與HEVC進行比較,平均BDBR上升不到0.1%的情況下,能減少30%的編碼時間。;With the advancement of technology and high requirement, multimedia devices that have high resolution started to rapidly increase in numbers. In order to compress the significant increasing of data storage effectively, HEVC utilize multiple techniques to efficiently decrease bitrate。Hence, in this thesis, we proposed SVM-based fast inter CU depth decision algorithm and SVM-based fast inter PU mode decision algorithm to reduce the computational complexity. In SVM-based fast inter CU depth decision algorithm, we can skip certain depth by using SVM with features, including motion vector variance, CBF of merge mode, neighboring CU depth to classify a CTU into depth 0, depth 0~1, depth 0~2 and depth 0~3. In SVM-based fast inter PU mode decision algorithm, we use SVM with features, including motion vector variance, skip flag, the information of neighboring RDO to classify whether do early termination at 2N×2N. At last, we combine two algorithm to compare with HEVC, the average BDBR rises by less than 0.1% and 30% encoding time saving.
    Appears in Collections:[通訊工程研究所] 博碩士論文

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