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    题名: 基於次巨集區塊固定取樣點SSAD預測SAD的移動估算法
    其它题名: A motion estimation method of predicting SAD based on sub-macro-block fixed-sample-points SSAD
    作者: 陳坤勝;Chen, Kun-sheng
    贡献者: 淡江大學電機工程學系碩士在職專班
    江正雄;Chiang, Jen-shiun
    关键词: 移動估算;固定取樣點;SAD預測;次巨集區塊;預測SAD;Sub-Macro-Block;Fixed-Sample-Points;SMB;Fixed-Sample-Points;Predictive SAD;Predicting SAD;SSAD;Motion estimation;Sub-sample SAD
    日期: 2008
    上传时间: 2010-01-11 07:01:41 (UTC+8)
    摘要: 移動估算演算法的優劣,對於動態影像壓縮的品質、計算複雜度與計算量有很大的影響,因此改善移動估算搜尋演算法的議題一直被討論著。過去有許多搜尋法不斷探討候選區塊各種不同比對的方法來搜尋移動向量,如眾所熟悉的全區塊搜尋法(Full Search)、三步搜尋法(Three Step Search)、四步搜尋法(Four Step Search)、鑽石搜尋法(Diamond Search)、Cross Diamond Search(CDS)等等。在即時影像研究中,如何得到良好的影像品質是的重要課題,除此之外對於計算的複雜度、計算量的大小、搜尋點數的多寡以及搜尋時間的長短也都是大家時常探討的重點方向。
    本篇論文是針對搜尋點數作為探討的主題,對候選區塊做次巨集區塊的分割,加上用固定取樣點的方式來大量減少像點的計算。一般而言,當大量減少像點取樣的同時,很容易產生取樣失真的風險,本篇論文善用候選區塊(Candidate Block)所分割成的次巨集區塊(Sub-Macro-Block;簡稱SMB),同時利用候選區塊(Candidate Block)與當前區塊(Current Block)之間存有區域關聯性的概念,以此來降低差異、降低干擾;並採用SMB固定取樣點的SSAD(Sub-sample SAD)來預測候選區塊的SAD,減少運算的點數、減少運算量。
    本論文是以預測的SAD套入不同搜尋法當中來搜尋移動向量(Motion Vector),最後實際計算的PSNR值並非預測值,是真實的PSNR。針對搜尋點的數降低、搜尋時間的改善以及所產生的PSNR結果,都是本論文著眼的重點。雖然降低平均搜尋點數與得到良好的影像品質是不容易完全兼得,然而本論文的演算法不但可以達成減少平均搜尋點數的目的,得以提升效能,同時也能得到不錯的影像品質。
    The motion estimation algorithm of video sequence always influences image compressed quality, computational complexity and computational loading. Therefore, the issue of macro blocks matching has been discussed for a long time. In the past, many search methods explored how to find the minimum SAD of candidate blocks in the search area, the Motion Vector, such as the well-known methods-Full Search, Three-Step Search, Four-Step Search, Diamond Search, and Cross Diamond Search, …, etc. However, the methods of Three-Step Search, Four-Step Search, Diamond Search, and Cross Diamond Search not only reduce blocks matching and calculation compared to the Full Search but also have good PSNR. Although it is important to have a good image quality at a real time video display, we should not ignore to reduce the computation complexity and computational loading.
    This research work focuses on the topic of the search points and run time reduction. We propose to use a few Fixed-Sample-Points of 9 Sub-Macro- Blocks (SMB) that are separated from Candidate Block to calculate average Sub-sample SAD (SSAD), and then to predict Candidate-Block SAD. The Predictive SAD can be used to combine any native search method algorithms for Motion Vector searching. It can reduce computation complexity and computational loading. Based on the localized relationship of Sub-sampling Fixed-Samples-Points and SMBs between Candidate Block and Current Block, we divide the Candidate Block into 9 SMBs to reduce the sub-sampling distortion risk. According to the PSNR simulation, this research work cannot reduce search points and search time but also get an acceptable image quality.
    显示于类别:[電機工程學系暨研究所] 學位論文

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