本文選題：視頻質(zhì)量 + QOE�。� 參考：《電子科技大學(xué)》2014年碩士論文

【摘要】：隨著計(jì)算機(jī)網(wǎng)絡(luò)的普及和視頻壓縮技術(shù)的提高,網(wǎng)絡(luò)多媒體應(yīng)用在生活中的發(fā)展越來(lái)越快,用戶也也越來(lái)越關(guān)心流媒體的質(zhì)量。受到網(wǎng)絡(luò)的影響,流媒體在傳輸過(guò)程中會(huì)發(fā)生數(shù)據(jù)丟失,產(chǎn)生各種視頻損傷、卡頓、馬賽克現(xiàn)象。這些損傷對(duì)用戶而言是不希望發(fā)生的,對(duì)于運(yùn)營(yíng)商而言希望能夠監(jiān)測(cè)到這些損傷的發(fā)生、對(duì)這些損傷進(jìn)行指標(biāo)化以及做出相應(yīng)的策略。以往的性能指標(biāo)主要是從丟包率、帶寬大小、延遲、抖動(dòng)等QoS參數(shù)來(lái)描述網(wǎng)絡(luò)服務(wù)性能。用戶作為流媒體應(yīng)用的直接使用者,有著最大的話語(yǔ)權(quán),他們的感受是服務(wù)質(zhì)量的最好說(shuō)明。QoE(Quality of Experience)是從用戶角度出發(fā)的評(píng)價(jià)指標(biāo),評(píng)價(jià)系統(tǒng)的整體性能。本文主要研究流媒體的測(cè)量與QOE分析,參考如今視頻質(zhì)量模型,提出了一種部分參考的客觀視頻質(zhì)量評(píng)估方法。主要從圖像壓縮技術(shù)出發(fā),針對(duì)圖像的空域和時(shí)域信息做特征提取操作,并對(duì)這些特征信息建立數(shù)學(xué)模型,最后綜合得出最終結(jié)果。對(duì)于空域的特征信息定義了能量變化值(EVD);時(shí)域特征信息使用廣義高斯密度函數(shù)。對(duì)于流媒體應(yīng)用的模擬主要以PlanetLab眾多節(jié)點(diǎn)為實(shí)驗(yàn)平臺(tái),開(kāi)發(fā)并部署可以進(jìn)行實(shí)時(shí)流媒體交互、實(shí)時(shí)流媒體模擬的工具,并基于提出的客觀視頻質(zhì)量評(píng)估算法實(shí)時(shí)監(jiān)控流媒體視頻的圖像質(zhì)量。流媒體經(jīng)過(guò)實(shí)驗(yàn)平臺(tái)的傳輸之后,會(huì)形成各種各樣的損傷情況。最后通過(guò)主觀評(píng)價(jià)得分來(lái)驗(yàn)證本方法的正確性。本文的半?yún)⒖荚u(píng)估方法通過(guò)在發(fā)送端得到原始視頻的特征信息,然后將這些特征信息發(fā)送到接收端對(duì)比發(fā)送端的特征信息會(huì)得到一個(gè)客觀的質(zhì)量評(píng)價(jià),這個(gè)評(píng)價(jià)結(jié)果可以用來(lái)提供給服務(wù)商作為網(wǎng)絡(luò)服務(wù)質(zhì)量的參考。
[Abstract]:With the popularization of computer network and the improvement of video compression technology, network multimedia applications are developing more and more rapidly in daily life, and users are more and more concerned about the quality of streaming media. Under the influence of the network, streaming media will lose data during the transmission process, resulting in a variety of video damage, Carton, mosaic phenomenon. These injuries are not expected to occur to users, and operators want to be able to monitor the occurrence of these injuries, to target these injuries and to make corresponding strategies. In the past, QoS parameters such as packet loss rate, bandwidth size, delay and jitter were used to describe network service performance. As the direct users of streaming media applications, users have the greatest right to speak. Their feeling is that the best explanation of quality of service .QoS of experience is the evaluation index from the point of view of users and the overall performance of the evaluation system. This paper mainly studies the measurement and QOE analysis of streaming media. Referring to the current video quality model, a partial reference objective video quality evaluation method is proposed. Based on the image compression technology, the feature extraction operation is done for the spatial and temporal information of the image, and the mathematical model of the feature information is established. Finally, the final results are obtained. The energy variation value is defined for the spatial feature information and the generalized Gao Si density function is used for the time domain feature information. The simulation of streaming media application is mainly based on many nodes of PlanetLab. The tools for real-time streaming media interaction and real-time streaming media simulation can be developed and deployed. Based on the proposed objective video quality evaluation algorithm, the video quality of streaming media video is monitored in real time. Streaming media after the transmission of the experimental platform, will form a variety of damage. Finally, the correctness of the method is verified by subjective evaluation scores. The semi-reference evaluation method in this paper obtains the feature information of the original video at the sender, and then sends the feature information to the receiving end to compare the feature information of the sender to get an objective quality evaluation. The evaluation results can be used as a reference for service providers as the quality of service on the network.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN919.8
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本文編號(hào)：1886022