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    Please use this identifier to cite or link to this item: http://ccur.lib.ccu.edu.tw/handle/A095B0000Q/599

    Title: 以空間、時間、深度、轉換、及時空特徵作無參考雙眼立體視訊品質評估;No-reference Stereoscopic Video Quality Assessment Using Spatial, Temporal, Depth, Transform, and Spatiotemporal Features
    Authors: 林美秀;LIN, MEI-HSIU
    Contributors: 資訊工程研究所
    Keywords: 支持向量回歸;特徵擷取;無參考;雙眼立體視訊評估;feature extraction;no-reference;stereoscopic video quality assessment;support vector regression
    Date: 2017
    Issue Date: 2019-07-17
    Publisher: 資訊工程研究所
    Abstract: 近年來,3D應用技術越來越普遍,因此人類對於雙眼立體視訊的品質越來越重視,換句話說雙眼立體視訊的品質評估方法將會被廣泛的使用,其中無參考的雙眼立體視訊的評估技術為最實用且有效的方式,因此本研究著重於無參考的技術,首先在雙眼立體視訊的五個不同領域中擷取能反映雙眼立體視訊品質的特徵,其中在空間域上擷取模糊程度、區塊程度、局部二值模式及邊緣的資訊,在時間域上擷取亮度、DC值及視差變化的資訊,在轉換域上擷取離散餘弦轉換及小波轉換中的特徵,在時空域上擷取方向梯度直方圖及三維離散餘弦的特徵,接著以直方圖量化的方式來統計每一種特徵,並對左右視角的特徵取平均,接著將所有特徵正規化到相同的分布,再利用特徵選取來刪除不必要的特徵以提升效能,最後透過支持向量回歸的技術來預估最後的雙眼立體視訊品質分數,由實驗結果可以表示,本研究所提出的方法在NAMA3DS1_COSPAD1資料庫中較優於另一個無參考的方法,並且可媲美其它三種完全參考及部分參考的方法。
    Recently, 3D technology application is more and more widespread. Thus, humans pay more attention on stereoscopic video quality. In other words, the stereoscopic video quality assessment approaches will be widely used. No-reference stereoscopic video quality assessment technology is the most useful and effective way. Hence, no-reference stereoscopic video quality assessment technology is mainly focused in this study. First, five domain features including spatial, temporal, depth, transform, and spatiotemporal features are extracted. On the spatial domain, blurriness, blockiness, local binary pattern (LBP), and edge information are extracted. On the temporal domain, variation information of luminance, DC values, and disparity are extracted. On the depth domain, disparity and depth motion are extracted. On the transform domain, discrete wavelet transform (DWT) and discrete cosine transform (DCT) information are extracted. On the spatiotemporal domain, histogram of gradient (HOG) and 3D-DCT information are extracted. Each feature vector is obtained by using histogram statistics and normalization to the same distribution. Then, the feature vectors from the left-view and right-view videos are averaged. Here, feature selection is applied to strike out the unnecessary features to improve the performance. Here, support vector regression (SVR) is applied to estimate the stereoscopic video quality score. Finally, experimental results show that the proposed approach is better than the other NR approach on NAMA3DS1_COSPAD1 database and compares favorably with others FR and RR approaches.
    Appears in Collections:[資訊工程學系] 學位論文

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