本文選題：視頻質(zhì)量評價 + 模糊效應(yīng)��； 參考：《中山大學(xué)》2014年碩士論文

【摘要】：隨著流媒體技術(shù)的發(fā)展與普及，網(wǎng)絡(luò)視頻成為了主流的多媒體載體。網(wǎng)絡(luò)視頻在流媒體傳輸過程中需要經(jīng)過壓縮、傳輸、解壓等過程，這類操作會造成視頻質(zhì)量損傷。網(wǎng)絡(luò)視頻的質(zhì)量決定著流媒體的服務(wù)質(zhì)量，因此越來越多的流媒體運營商需要一種直觀準(zhǔn)確的基于流媒體的視頻質(zhì)量評價模型以實時獲取網(wǎng)絡(luò)視頻質(zhì)量值，從而掌握當(dāng)前流媒體的性能，并能選擇合適的流媒體服務(wù)器以及對流媒體技術(shù)進行提升。因此，對流媒體視頻的質(zhì)量評價的研究有著十分重要的意義。 由于視頻質(zhì)量的無參考評價法無需參考原始視頻，且適用于批量處理及具有較好的時效性。本文主要對視頻質(zhì)量的無參考評價法進行深入研究，并將本文使用的評價法運用于流媒體系統(tǒng)中。 首先，提出基于線性預(yù)測誤差的模糊效應(yīng)度量法。使用最小二乘法求出圖像分塊的線性組合，使用該線性組合去計算像素預(yù)測值與原始值的差值作為模糊效應(yīng)度量的特征值。 其次，，改進基于周期性塊檢測的方塊效應(yīng)度量法和基于小波變換的噪聲效應(yīng)度量法。對塊效應(yīng)的度量增加三個限制條件以更好的區(qū)別真實紋理與塊紋理，從而提高塊效應(yīng)度量的可靠度。對噪聲效應(yīng)的度量采用三種帶有浮動閾值的小波處理，提高了噪聲效應(yīng)度量的有效性。 然后，使用機器學(xué)習(xí)法整合三類失真效應(yīng)，給出視頻質(zhì)量綜合評價方法。結(jié)合機器學(xué)習(xí)法對帶有主觀評價值的數(shù)據(jù)庫進行訓(xùn)練，并把通過機器學(xué)習(xí)法總結(jié)出不同效應(yīng)所引起的不同視覺感受的特性運用于三類失真效應(yīng)的整合上，最終給出視頻質(zhì)量綜合評價方法。 最后，實現(xiàn)基于流媒體的視頻質(zhì)量評價模型及谷歌視頻搜索插件。結(jié)合流媒體技術(shù)、網(wǎng)絡(luò)視頻爬蟲技術(shù)、視頻關(guān)鍵幀提取技術(shù)以及視頻質(zhì)量綜合評價法，實現(xiàn)基于流媒體的視頻質(zhì)量評價模型。為證明模型的可用性以及時效性，將結(jié)合谷歌應(yīng)用擴展，實現(xiàn)基于谷歌視頻搜索的視頻質(zhì)量評價插件，根據(jù)用戶的視頻搜索結(jié)果，實時調(diào)用評價模型的數(shù)據(jù)庫并將結(jié)果通過網(wǎng)頁反饋給用戶。
[Abstract]:With the development and popularization of streaming media technology, network video has become the mainstream multimedia carrier. In the process of streaming media transmission, network video needs to be compressed, transmitted, decompressed and so on. This kind of operation will cause video quality damage. The quality of network video determines the quality of service of streaming media, so more and more streaming media operators need an intuitive and accurate video quality evaluation model based on streaming media in order to obtain the quality of network video in real time. In order to master the current streaming media performance, and can choose the appropriate streaming media server and streaming media technology to improve. Therefore, the research on the quality evaluation of streaming media video is of great significance. Because the non-reference evaluation method of video quality does not need to refer to the original video, it is suitable for batch processing and has good timeliness. In this paper, the non-reference evaluation method of video quality is studied deeply, and the evaluation method used in this paper is applied to streaming media system. Firstly, a fuzzy effect measurement method based on linear prediction error is proposed. The linear combination of image blocks is obtained by using the least square method, and the difference between the pixel prediction value and the original value is calculated as the eigenvalue of the fuzzy effect measurement. Secondly, the block effect measurement method based on periodic block detection and the noise effect measurement method based on wavelet transform are improved. In order to improve the reliability of block effect measurement, three restrictions are added to distinguish real texture from block texture. Three kinds of wavelet processing with floating threshold are used to measure noise effect, which improves the effectiveness of noise effect measurement. Then, the machine learning method is used to integrate three kinds of distortion effects, and a comprehensive evaluation method of video quality is presented. Combined with machine learning method, the database with subjective evaluation value is trained, and the characteristics of different visual feelings caused by different effects are summed up by machine learning method and applied to the integration of three kinds of distortion effects. Finally, a comprehensive evaluation method of video quality is presented. Finally, the video quality evaluation model based on streaming media and Google video search plug-in are implemented. The video quality evaluation model based on streaming media is realized by combining streaming media technology, network video crawler technology, video key frame extraction technology and video quality comprehensive evaluation method. In order to prove the usability and timeliness of the model, a video quality evaluation plug-in based on Google Video search will be implemented in combination with the Google application extension, according to the user's video search results, The database of the evaluation model is called in real time and the result is fed back to the user through the web page.
【學(xué)位授予單位】：中山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN919.8

【參考文獻】

相關(guān)期刊論文 前2條

1 李永強;沈慶國;朱江;汪莉;;數(shù)字視頻質(zhì)量評價方法綜述[J];電視技術(shù);2006年06期

2 姚杰;譚建明;陳婧;黃劍鋒;;基于小波變換的無參考視頻質(zhì)量評價[J];重慶工商大學(xué)學(xué)報(自然科學(xué)版);2012年09期

本文編號：2065130