【摘要】：交通視頻作為智能交通系統(tǒng)的重要組成部分有著廣泛的應(yīng)用,不斷產(chǎn)生的龐大交通視頻數(shù)據(jù),給交通視頻的存儲(chǔ)帶來(lái)不小的挑戰(zhàn),如何對(duì)交通視頻進(jìn)行壓縮就成為一個(gè)重要的研究課題。當(dāng)前交通視頻的壓縮仍采用傳統(tǒng)的基于統(tǒng)計(jì)的編碼壓縮方式,而未能充分利用交通視頻的特點(diǎn)對(duì)其進(jìn)行壓縮。交通視頻具有背景穩(wěn)定、敏感區(qū)域明確、圖像紋理復(fù)雜等特點(diǎn),并且交通視頻監(jiān)控通常安裝在戶外,交通視頻圖像會(huì)受到戶外光照變化、天氣變化的影響。如何充分利用交通視頻的特點(diǎn),研究出適合交通視頻特點(diǎn)的壓縮方法就成為重要的研究課題。壓縮感知理論為交通視頻的壓縮提供了一種有益的思路。由于交通視頻具有很大的空間冗余和時(shí)間冗余,針對(duì)這些特點(diǎn),采用壓縮感知理論就可以有效地對(duì)交通視頻進(jìn)行觀測(cè)壓縮。根據(jù)以上思路,本文對(duì)基于壓縮感知的交通視頻壓縮方法進(jìn)行了研究,所做的具體工作如下:(1)在理解壓縮感知理論和相關(guān)定理的基礎(chǔ)上,重點(diǎn)研究了基于K-SVD算法的交通圖像壓縮感知重建,針對(duì)K-SVD算法時(shí)間復(fù)雜度高、重構(gòu)圖像質(zhì)量一般的缺點(diǎn),本文提出一種基于小波樹(shù)變迭代次數(shù)K-SVD算法。仿真實(shí)驗(yàn)結(jié)果表明,基于小波樹(shù)變迭代次數(shù)K-SVD算法與原K-SVD算法相比,PSNR值提高2dB左右,算法運(yùn)行時(shí)間降低了15%左右。(2)交通視頻預(yù)處理是交通視頻壓縮編碼框架設(shè)計(jì)的基礎(chǔ)。在預(yù)處理部分,首先對(duì)交通視頻進(jìn)行背景建模,背景提取采用混合高斯模型,與均值法相比,所提取的背景更干凈清晰;其次對(duì)視頻進(jìn)行背景更新,本文使用一種基于分塊分類的背景更新方法,在此背景更新算法中,采用三幀差分法獲得差分圖像,自適應(yīng)迭代閾值方法確定分類所需的閾值,并用所提取的背景,得到更新背景。第三,對(duì)交通視頻進(jìn)行場(chǎng)景分類,對(duì)交通視頻場(chǎng)景的晝夜進(jìn)行判斷,并對(duì)夜間圖像進(jìn)行增強(qiáng)。最后,為提高視頻壓縮率及視頻質(zhì)量,本文提出一種變采樣率計(jì)算模型:根據(jù)分塊壓縮感知理論,采用擬合經(jīng)驗(yàn)函數(shù)的變采樣率算法,并在此基礎(chǔ)上描述一種適用于圖像組(Group of Picture,GOP)的變采樣率觀測(cè)壓縮過(guò)程。(3)設(shè)計(jì)出一種基于壓縮感知的交通視頻編碼框架,并仿真驗(yàn)證此框架對(duì)視頻的壓縮性能及可用性。
[Abstract]:As an important part of intelligent transportation system, traffic video has a wide range of applications, and the huge traffic video data is constantly generated, which brings a lot of challenges to the storage of traffic video. How to compress traffic video becomes an important research topic. The current traffic video compression still uses the traditional statistical-based coding compression mode, but fails to make full use of the characteristics of traffic video to compress it. Traffic video has the characteristics of stable background, clear sensitive area, complex image texture and so on. Traffic video surveillance is usually installed outdoors, and traffic video image will be affected by outdoor illumination and weather change. How to make full use of the characteristics of traffic video and study the compression method suitable for the characteristics of traffic video becomes an important research topic. The theory of compression perception provides a useful idea for the compression of traffic video. Because traffic video has a lot of space redundancy and time redundancy, according to these characteristics, the compression perception theory can be used to observe and compress traffic video effectively. According to the above ideas, this paper studies the traffic video compression method based on compression perception. The concrete work is as follows: (1) on the basis of understanding the compression perception theory and related theorems, This paper focuses on the traffic image compression perceptual reconstruction based on K-SVD algorithm. In view of the disadvantages of high time complexity and general image quality of K-SVD algorithm, this paper proposes a K-SVD algorithm based on wavelet tree variation iteration times. The simulation results show that, compared with the original K-SVD algorithm, the PSNR value of the K-SVD algorithm based on wavelet tree variation iteration times is about 2dB higher than that of the original PSNR algorithm. The running time of the algorithm is reduced by about 15%. (2) Traffic video preprocessing is the basis of the design of traffic video compression coding framework. In the pre-processing part, first of all, the traffic video background modeling, background extraction using the mixed Gao Si model, compared with the mean method, the extracted background is cleaner and clearer; Secondly, a background update method based on block classification is used in this paper. In the background updating algorithm, the difference image is obtained by three-frame difference method, and the adaptive iterative threshold method is used to determine the threshold required for classification. The background is updated with the extracted background. Thirdly, it classifies the traffic video scene, judges the day and night of the traffic video scene, and enhances the night image. Finally, in order to improve the video compression rate and video quality, this paper proposes a variable sampling rate calculation model: according to the theory of block compression perception, the variable sampling rate algorithm based on fitting empirical function is adopted. On this basis, a variable sampling rate observation compression process suitable for (Group of Picture,GOP is described. (3) A traffic video coding framework based on compression perception is designed. The video compression performance and availability of this framework are verified by simulation.
【學(xué)位授予單位】：長(zhǎng)安大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：U495;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李艷平;;近五年國(guó)內(nèi)智慧交通研究綜述[J];中國(guó)高新技術(shù)企業(yè);2016年07期

2 李慶忠;劉清;;基于小波變換的低照度圖像自適應(yīng)增強(qiáng)算法[J];中國(guó)激光;2015年02期

3 柯常志;徐鵬;;基于視頻檢測(cè)的高清攝像機(jī)在智能交通監(jiān)控系統(tǒng)中的應(yīng)用[J];工業(yè)控制計(jì)算機(jī);2015年01期

4 金煒;王文龍;閆河;;變采樣率的磁共振圖像分塊壓縮感知[J];光電子·激光;2014年12期

5 蔣行國(guó);覃陽(yáng);韋保林;;基于改進(jìn)K-SVD的磁共振圖像去噪算法[J];科技導(dǎo)報(bào);2014年08期

6 黃國(guó)浪;潘捷;許曉楠;;基于混合高斯模型的道路背景提取方法研究[J];山東交通學(xué)院學(xué)報(bào);2014年01期

7 廖律超;蔣新華;鄒復(fù)民;賴宏圖;;基于交通視頻的交通擁堵?tīng)顟B(tài)自動(dòng)識(shí)別方法[J];公路交通科技;2014年01期

8 譚筠梅;王履程;雷濤;王小鵬;;城市軌道交通智能視頻分析關(guān)鍵技術(shù)綜述[J];計(jì)算機(jī)工程與應(yīng)用;2014年04期

9 羅暉;王世昌;褚紅亮;楊成武;;交通監(jiān)控中基于壓縮感知的WMSN視頻編解碼[J];華東交通大學(xué)學(xué)報(bào);2013年01期

10 黃江林;劉紅;陶少杰;;一種改進(jìn)的基于K-SVD字典的圖像修復(fù)算法[J];安徽大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年03期

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