【摘要】：目前對(duì)于普通家庭用戶(hù)而言,在線觀看三維視頻的效果無(wú)法令人滿(mǎn)意,不僅畫(huà)面模糊而且三維立體感較差。這一方面是因?yàn)槿S視頻的數(shù)據(jù)量巨大,過(guò)度的壓縮對(duì)三維視頻的立體感與深度感體驗(yàn)影響很大。另一方面,如何對(duì)三維視頻的質(zhì)量進(jìn)行合理的評(píng)價(jià)是一個(gè)還沒(méi)有被很好地解決的問(wèn)題。三維視頻的失真類(lèi)型與傳統(tǒng)的二維視頻存在區(qū)別,直接套用己有的視頻質(zhì)量評(píng)價(jià)方法效果不佳,且三維視頻的用戶(hù)質(zhì)量體驗(yàn)還包含了三維視頻深度感質(zhì)量等更復(fù)雜的影響因素。深度圖作為一種三維視頻輔助信息目前正在得到越來(lái)越廣泛的應(yīng)用。對(duì)于基于深度圖的三維視頻系統(tǒng)而言,虛擬視點(diǎn)的視頻圖像質(zhì)量好壞將會(huì)影響整個(gè)系統(tǒng)的用戶(hù)體驗(yàn)質(zhì)量。第二章主要對(duì)存在紋理/深度壓縮失真的虛擬繪制視點(diǎn)視頻的主觀和客觀質(zhì)量評(píng)價(jià)進(jìn)行了研究。主觀數(shù)據(jù)集的設(shè)計(jì)原則是確保用于測(cè)試的虛擬繪制視頻的質(zhì)量覆蓋范圍足夠廣并且彼此之間具有一定的質(zhì)量區(qū)分度。為此,每個(gè)紋理/深度視頻壓縮量化參數(shù)組合都是從大量的候選中進(jìn)行仔細(xì)挑選的。所建立的虛擬繪制視點(diǎn)視頻主觀質(zhì)量評(píng)價(jià)數(shù)據(jù)集目前己被眾多國(guó)內(nèi)外的知名研究機(jī)構(gòu)所使用。另外,還提出了一種全參考的虛擬繪制視點(diǎn)視頻客觀質(zhì)量評(píng)價(jià)算法。該算法主要關(guān)注由深度圖壓縮以及視點(diǎn)繪制過(guò)程本身帶來(lái)的時(shí)域閃爍失真。實(shí)驗(yàn)結(jié)果表明,所提出的算法在完整數(shù)據(jù)集上的性能要顯著優(yōu)于現(xiàn)有的客觀視頻質(zhì)量評(píng)價(jià)算法,在時(shí)域閃爍失真較為明顯的子集上相對(duì)于其它算法的優(yōu)勢(shì)更加明顯。第三章重點(diǎn)研究了三維視頻的深度感質(zhì)量評(píng)價(jià),通過(guò)主觀實(shí)驗(yàn)對(duì)圖像失真如何影響三維立體視頻的深度感知質(zhì)量進(jìn)行了深入探索,實(shí)驗(yàn)數(shù)據(jù)集既包括了對(duì)稱(chēng)失真的情況,又包括了非對(duì)稱(chēng)失真的情況。主觀實(shí)驗(yàn)的結(jié)果證明圖像細(xì)節(jié)的丟失會(huì)影響深度感知。主觀實(shí)驗(yàn)得到的三維立體視頻深度感知質(zhì)量分?jǐn)?shù)以及圖像質(zhì)量分?jǐn)?shù)已經(jīng)對(duì)外公開(kāi)發(fā)布。同時(shí),該章還提出了一種客觀評(píng)價(jià)算法用于測(cè)量圖像失真引起的深度感知質(zhì)量下降。實(shí)驗(yàn)結(jié)果表明,所提出的算法能夠準(zhǔn)確地預(yù)測(cè)深度感知質(zhì)量的下降。第四章提出了一種深度視頻感知優(yōu)化編碼算法。該算法首先通過(guò)一種低復(fù)雜度的視頻質(zhì)量評(píng)價(jià)方法計(jì)算虛擬繪制視點(diǎn)視頻的空域失真和時(shí)域失真。該算法將低復(fù)雜度的視頻質(zhì)量評(píng)價(jià)算法計(jì)算得到的虛擬繪制視點(diǎn)失真作為深度視頻編碼率失真優(yōu)化過(guò)程中的失真度量準(zhǔn)則,并重新推導(dǎo)了目標(biāo)函數(shù)中的拉格朗日乘子。實(shí)驗(yàn)結(jié)果表明所提出的深度視頻感知優(yōu)化編碼算法可以顯著地減少虛擬視點(diǎn)視頻的時(shí)域閃爍失真。所提出的低復(fù)雜度視頻評(píng)價(jià)算法對(duì)虛擬視點(diǎn)視頻主觀質(zhì)量的預(yù)測(cè)準(zhǔn)確度超過(guò)了現(xiàn)有的主流視頻圖像質(zhì)量評(píng)價(jià)算法�；旧峡梢詿o(wú)縫整合到現(xiàn)有的三維視頻編碼器當(dāng)中。
[Abstract]:At present, the effect of online viewing 3D video is not satisfactory for ordinary home users, not only the picture is blurred, but also the 3D stereoscopic sense is poor. This is due to the huge amount of 3D video data, the excessive compression of 3D video 3D sense and depth of experience has a great impact. On the other hand, how to evaluate the quality of 3D video is a problem that has not been solved well. The distortion type of 3D video is different from that of traditional 2D video, and the evaluation method of video quality is not good. The user quality experience of 3D video also includes more complicated factors such as depth quality of 3D video. As a kind of 3D video aided information, depth map is being used more and more widely. For 3D video system based on depth map, the quality of video image from virtual viewpoint will affect the quality of user experience of the whole system. In chapter 2, the subjective and objective quality evaluation of virtual view video with texture / depth compression distortion is studied. The design principle of the subjective data set is to ensure that the quality coverage of the virtual rendering video for testing is wide enough and that there is a certain degree of quality distinction between each other. Therefore, each texture / depth video compression quantization parameter combination is carefully selected from a large number of candidates. The virtual rendering view video subjective quality evaluation data set has been used by many famous research institutions at home and abroad. In addition, an objective quality evaluation algorithm for virtual video rendering based on full reference is proposed. The algorithm focuses on the time domain scintillation distortion caused by depth map compression and view rendering itself. The experimental results show that the proposed algorithm is superior to the existing objective video quality evaluation algorithm on the complete data set, and the superiority of the proposed algorithm is more obvious than other algorithms on the subset with obvious time-domain scintillation distortion. In the third chapter, the depth perception quality evaluation of 3D video is studied, and how image distortion affects depth perception quality of 3D video is deeply explored through subjective experiments. The experimental data set includes symmetric distortion. It also includes asymmetric distortion. The results of subjective experiments show that the loss of image details will affect depth perception. The depth perception quality scores and image quality scores obtained from subjective experiments have been published publicly. At the same time, an objective evaluation algorithm is proposed to measure the depth-sensing degradation caused by image distortion. Experimental results show that the proposed algorithm can accurately predict the decline of depth perception quality. In chapter 4, a depth video perceptual optimization coding algorithm is proposed. Firstly, a low complexity video quality evaluation method is used to calculate the spatial and temporal distortion of virtual rendering view video. In this algorithm, the virtual rendering viewpoint distortion obtained by the low complexity video quality evaluation algorithm is used as the distortion criterion in the process of the rate distortion optimization of the depth video coding, and the Lagrangian multiplier in the objective function is rededuced. Experimental results show that the proposed depth video perceptual optimization coding algorithm can significantly reduce the time domain flicker distortion of virtual view video. The prediction accuracy of the proposed low complexity video evaluation algorithm for the subjective quality of virtual view video is higher than that of the existing mainstream video image quality evaluation algorithms. Basically can seamlessly integrate into the existing three-dimensional video encoder.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP391.41

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