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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/9339


    Title: 快速多階連續消除移動預估演算法應用於H.26L視訊編碼標準;Fast Multi-level Successive Elimination Algorithm for Motion Estimation in H.26L
    Authors: 蔡國基;Kuo-Chi Tsai
    Contributors: 電機工程研究所
    Keywords: H.26L視訊編碼標準;快速移動預估演算法;多階連續消除演算法(MSEA);精確位移向量;中斷決策;H.26L Video Coding;Fast Motion Estimation;Mult
    Date: 2002-06-24
    Issue Date: 2009-09-22 11:45:44 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 移動預估在視訊壓縮編碼上一直扮演著重要的角色,其最主要的目的,是要消除視訊畫面在時間上的相關性,移動預估演算法的好壞,對整個視訊壓縮品質有極大的影響。 H.26L是目前已發展完成並且最有效率的視訊編碼標準,它使用了多重模式,即不同區塊大小的移動預估,來改進及增進移動向量的精確度。然而,在H.26L視訊編碼標準上,使用全域搜尋演算法(Full Search)會造成龐大的計算量。為了要降低全域搜尋演算法的複雜度,本論文提出快速多階連續消除演算法(FMSEA)於H.26L視訊編碼標準,來解決H.26L在多重模式移動預估上的搜尋。本論文所提出的方法,最主要是修改多階連續消除演算法,並且結合精確位移向量(motion refinement)及利用中斷決策來判斷是否繼續搜尋4x8、8x4及4x4這三個模式。實驗的結果顯示,本論文所提出的快速多階連續消除演算法,有效地降低複雜度並且畫面品質極接近全域搜尋法(Full Search),適合運用在視訊壓縮標準H.26L上。 Motion estimation plays an extremely important role in the video coding. The objective of the motion estimation is to remove the temporal redundancy between video frames so that the motion compensated frames can be coded efficiently. H.26L video coding is the most efficient coding standard currently available. It uses multi-mode with variable block-size motion estimation to improve the accuracy. However, the conventional full search algorithm will be a heavy computational load in this situation. To reduce the complexity, we propose a fast multi-level successive elimination algorithm (FMSEA) for H.26L multi-mode motion estimation search. The proposed method is mainly based on the combination of a modified multi-level successive elimination algorithm (MSEA) with a motion refinement approach and a half-stop decision that skips the 8x4, 4x8, and 4x4 sub-block motion searches. Experimental results show that FMSEA is very efficient in terms of the computational speedup and video reconstruction quality for H.26L.
    Appears in Collections:[Graduate Institute of Electrical Engineering] Electronic Thesis & Dissertation

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