【摘要】：隨著計(jì)算機(jī)的普及和網(wǎng)絡(luò)技術(shù)的日益成熟,與視頻相關(guān)的網(wǎng)絡(luò)多媒體應(yīng)用得到了迅猛的發(fā)展。視頻在壓縮、傳輸和存儲(chǔ)的過(guò)程中會(huì)受到數(shù)據(jù)損失,引入各種失真效應(yīng)。為了獲得更好的視頻主觀效果,需要評(píng)價(jià)失真視頻的質(zhì)量,根據(jù)評(píng)價(jià)結(jié)果調(diào)整編碼器和傳輸信道的相關(guān)參數(shù)。在目前大多數(shù)網(wǎng)絡(luò)多媒體系統(tǒng)中,視頻質(zhì)量評(píng)價(jià)已經(jīng)成為不可或缺的重要組成部分。人類(lèi)對(duì)這些失真視頻的主觀質(zhì)量評(píng)價(jià)被公認(rèn)為最精確的方法,但是這個(gè)過(guò)程消耗大量的人力和時(shí)間,不適合大規(guī)模實(shí)際應(yīng)用。因此,通過(guò)設(shè)計(jì)數(shù)學(xué)模型對(duì)失真視頻進(jìn)行智能化分析,從而計(jì)算視頻質(zhì)量的客觀質(zhì)量評(píng)價(jià)方法,成為當(dāng)前國(guó)際性的研究熱點(diǎn)。根據(jù)對(duì)原始視頻的依賴(lài)程度,視頻客觀質(zhì)量評(píng)價(jià)方法可以分為全參考型、部分參考型和無(wú)參考型三類(lèi)。由于全參考型和部分參考型評(píng)價(jià)方法都需要額外的帶寬來(lái)傳輸原始視頻及相關(guān)信息,其實(shí)用價(jià)值非常有限。相比之下,無(wú)參考質(zhì)量評(píng)價(jià)方法不需要依賴(lài)任何與原始視頻相關(guān)的信息,直接根據(jù)待評(píng)價(jià)視頻的信息計(jì)算視頻質(zhì)量,具有更好的靈活性和適應(yīng)性,以及更廣泛的應(yīng)用價(jià)值。本文正是在這樣的背景下,展開(kāi)了對(duì)無(wú)參考視頻質(zhì)量評(píng)價(jià)方法的研究。 第一章緒論部分首先闡述了選題的意義,然后綜述國(guó)內(nèi)外研究現(xiàn)狀并作相應(yīng)的總結(jié),最后介紹了本課題的主要研究?jī)?nèi)容和論文結(jié)構(gòu)。 第二章對(duì)像素域無(wú)參考視頻質(zhì)量評(píng)價(jià)方法進(jìn)行研究,提出了一種基于失真度估計(jì)的無(wú)參考視頻質(zhì)量評(píng)價(jià)方法。該方法首先統(tǒng)計(jì)相鄰像素點(diǎn)之間亮度差值的方差,作為視頻局部失真度。再對(duì)視頻進(jìn)行高斯濾波,計(jì)算濾波后的細(xì)節(jié)損失,得到視頻全局失真度。然后,結(jié)合這兩者估計(jì)視頻整體的失真度。同時(shí),通過(guò)幀內(nèi)預(yù)測(cè)和幀間預(yù)測(cè)計(jì)算視頻復(fù)雜度,反映視頻內(nèi)容特征。最后,綜合視頻失真度和復(fù)雜度得到視頻客觀質(zhì)量。 第三章對(duì)壓縮域無(wú)參考視頻質(zhì)量評(píng)價(jià)方法進(jìn)行研究,提出了一種基于視頻內(nèi)容復(fù)雜度的無(wú)參考視頻質(zhì)量評(píng)價(jià)方法。該方法首先通過(guò)分析碼率、壓縮率與視頻場(chǎng)景之間的關(guān)系,得到視頻內(nèi)容復(fù)雜度。利用宏塊模式、量化系數(shù)、運(yùn)動(dòng)矢量和消耗碼流等編碼信息計(jì)算量化、運(yùn)動(dòng)和碼流分配影響因子,分別代表視頻壓縮中量化、運(yùn)動(dòng)搜索和碼率控制這三個(gè)關(guān)鍵環(huán)節(jié)對(duì)壓縮后視頻質(zhì)量的影響。然后,在分析這三個(gè)影響因子各自對(duì)視頻質(zhì)量的反饋的基礎(chǔ)上,結(jié)合視頻內(nèi)容復(fù)雜度,建立基于視頻內(nèi)容復(fù)雜度的視頻質(zhì)量評(píng)價(jià)模型。最后,對(duì)整個(gè)視頻進(jìn)行場(chǎng)景切換檢測(cè),將其分為不同的場(chǎng)景片段,并用此模型評(píng)價(jià)每個(gè)視頻場(chǎng)景的客觀質(zhì)量,綜合得到整個(gè)視頻的質(zhì)量。 第四章對(duì)視覺(jué)感知特性的基本原理及其在視頻質(zhì)量評(píng)價(jià)領(lǐng)域中的應(yīng)用進(jìn)行研究,提出了一種基于視覺(jué)感知特性的無(wú)參考視頻質(zhì)量評(píng)價(jià)方法。首先,根據(jù)人類(lèi)視覺(jué)系統(tǒng)(Human Visual System, HVS)對(duì)視頻場(chǎng)景的感知特性,利用對(duì)比度、紋理特征得到空域感知特性,利用運(yùn)動(dòng)強(qiáng)度對(duì)比度、運(yùn)動(dòng)方向得到時(shí)域感知特性。然后采用融合時(shí)域和空間域特性的方式,建立視覺(jué)注意模型。最后用該模型改善已有的無(wú)參考視頻質(zhì)量評(píng)價(jià)方法,根據(jù)視覺(jué)注意模型對(duì)視頻的不同部分進(jìn)行加權(quán)計(jì)算,提高評(píng)價(jià)結(jié)果的準(zhǔn)確度。 第五章在像素域和壓縮域無(wú)參考視頻質(zhì)量評(píng)價(jià)的基礎(chǔ)上,結(jié)合視覺(jué)感知特性,提出了一種基于視覺(jué)感知特性的雙域無(wú)參考視頻質(zhì)量評(píng)價(jià)方法。該方法首先在壓縮域提取碼流中的編碼信息,并利用這些信息建立壓縮域視頻質(zhì)量評(píng)價(jià)子模型,預(yù)測(cè)失真視頻與原始視頻的相似度。然后,在像素域檢測(cè)兩種常見(jiàn)的失真效應(yīng),塊效應(yīng)和模糊效應(yīng)的失真程度,并利用第四章提出的時(shí)空聯(lián)合視覺(jué)注意模型,對(duì)失真效應(yīng)檢測(cè)結(jié)果進(jìn)行加權(quán),得到視頻失真度。最后,結(jié)合視頻相似度和失真度給出視頻整體的質(zhì)量評(píng)價(jià)。 第六章總結(jié)了本論文的研究成果和創(chuàng)新點(diǎn),并提出了進(jìn)一步研究的方向和任務(wù)。
[Abstract]:With the popularization of the computer and the increasing maturity of the network technology, the network multimedia application related to the video has been developed rapidly. The video is lost in the process of compression, transmission, and storage and introduces various distortion effects. In order to obtain a better subjective effect of the video, the quality of the distorted video needs to be evaluated, and the relevant parameters of the encoder and the transmission channel are adjusted according to the evaluation result. In most of the current network multimedia systems, video quality evaluation has become an integral and important component. The subjective quality evaluation of these distorted videos has been recognized as the most accurate method, but this process consumes a lot of manpower and time and is not suitable for large-scale practical applications. Therefore, the objective quality evaluation method of the video quality is calculated by the intelligent analysis of the distorted video by the design mathematical model, which becomes the current international research hotspot. according to the degree of dependence on the original video, the video objective quality evaluation method can be divided into a full-reference type, a partial reference type and a non-reference type. Since the full-reference and partial-reference-type evaluation methods require additional bandwidth to transmit the original video and related information, the value is very limited. In contrast, the non-reference quality evaluation method does not need to rely on any information related to the original video, and directly calculates the video quality according to the information of the video to be evaluated, and has better flexibility and adaptability and wider application value. It is in this background that the research on the method of no-reference video quality evaluation is carried out. The first chapter introduces the significance of the topic, then summarizes the domestic and foreign research status and the corresponding summary, and finally introduces the main research contents and the thesis of the subject. In the second chapter, the non-reference video quality evaluation method for pixel domain is studied, and a non-reference video quality based on distortion estimation is proposed. The method comprises the following steps of: firstly, counting the variance of the brightness difference value among the adjacent pixel points, local distortion is obtained, the video is subjected to Gaussian filtering, the filtered detail loss is calculated, and the video is obtained Global distortion. Then, combine the two to estimate the video the distortion of the body is calculated by the intra-frame prediction and inter-frame prediction, and the video complexity is reflected, and finally, the comprehensive video distortion degree and the complexity are obtained. The third chapter studies the method of non-reference video quality evaluation in the compressed domain, and puts forward a non-reference based on the complexity of video content. The method comprises the following steps of: firstly, analyzing the relationship between a code rate, a compression rate and a video scene, the coding information such as the macro block mode, the quantization coefficient, the motion vector and the consumption code stream is used for calculating the quantization, the motion and the code stream allocation influence factors, and then, on the basis of analyzing the feedback of the three influence factors on the video quality, combining the video content complexity and establishing the video content complexity, and finally, carrying out scene switching detection on the whole video, dividing the whole video into different scene segments, and evaluating the objective quality of each video scene by using the model, The fourth chapter studies the basic principle of visual perception and its application in the field of video quality evaluation. The method for evaluating the non-reference video quality comprises the following steps of: firstly, according to the perception characteristics of a human visual system (HVS) on a video scene, in the dynamic direction, the time-domain sensing characteristics are obtained, and finally, using the model to improve the existing non-reference video quality evaluation method, and carrying out weight calculation on different parts of the video according to the visual attention model, In chapter 5, based on the non-reference video quality evaluation of the pixel domain and the compressed domain, a visual perception characteristic is proposed based on the visual perception characteristic. The method comprises the following steps of: extracting the encoding information in a code stream in a compressed domain, and establishing a compressed domain video quality evaluation sub-model by using the information, wherein the method The similarity of the distorted video to the original video is measured. Then, the distortion degree of two common distortion effects, block effect and fuzzy effect is detected in the pixel domain, and the time-space combined visual attention model proposed in the fourth chapter is used to detect the distortion effect. the result is weighted to obtain the video distortion, and finally, the video similarity and the video similarity are combined, In chapter 6, the research results and the innovation of this paper are summarized in the sixth chapter.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TP391.41

【引證文獻(xiàn)】

相關(guān)期刊論文 前1條

1 周傳興;涂巧玲;張杰;張晗;;基于視覺(jué)感知的網(wǎng)絡(luò)視頻質(zhì)量評(píng)價(jià)方法研究[J];微型機(jī)與應(yīng)用;2017年11期

相關(guān)碩士學(xué)位論文 前6條

1 崔明明;視頻底層特征選取及其與觀眾評(píng)價(jià)的相關(guān)分析[D];東北電力大學(xué);2016年

2 賈琳;基于DSP的視頻質(zhì)量監(jiān)測(cè)系統(tǒng)設(shè)計(jì)[D];黑龍江大學(xué);2015年

3 盧培磊;無(wú)參考視頻平滑度評(píng)價(jià)方法的研究[D];武漢工程大學(xué);2015年

4 袁媛;基于顯著性區(qū)域的視頻質(zhì)量評(píng)價(jià)[D];北京郵電大學(xué);2015年

5 馬瑞澤;壓縮立體視頻質(zhì)量客觀評(píng)價(jià)方法研究[D];天津大學(xué);2014年

6 劉立冬;基于H.264壓縮域的視頻水印算法研究[D];浙江大學(xué);2013年

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本文編號(hào)：2381784