本文選題：H.264 + JND視覺模型��； 參考：《電子科技大學》2014年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)技術(shù)和流媒體技術(shù)的迅猛發(fā)展,使實時視頻通信技術(shù)也取得了很大的進步。今天,實時視頻傳輸系統(tǒng)已經(jīng)廣泛應(yīng)用于軍事指揮,安全監(jiān)控,網(wǎng)絡(luò)社交等多個領(lǐng)域[1]。與此同時,人們對視頻的傳輸速度和視頻的質(zhì)量的要求也越來越高。要在有限的帶寬條件下流暢的傳輸高質(zhì)量的視頻圖像,就必須采用高性能的硬件和高效的編碼算法。H.264是適用于低碼率視頻傳輸?shù)男乱淮曨l壓縮標準,它比以往的編碼標準的編碼效率都要高,編碼質(zhì)量也更好,網(wǎng)絡(luò)適應(yīng)性更強。本文首先對H.264編碼標準進行了闡述,介紹了其中幾個比較關(guān)鍵的編碼技術(shù),例如幀間預測、幀內(nèi)預測、DCT變換和量化等,為本文后面將JND模型應(yīng)用于H.264編碼算法奠定了理論基礎(chǔ)。隨后,本文介紹了人眼的亮度特性、空間頻率敏感特性以及對比度掩蔽特性,然后根據(jù)Wei提出的應(yīng)用于JPEG標準的JND模型,提出一種改進的應(yīng)用于H.264標準的JND模型。本文JND模型的亮度自適應(yīng)因子不是線性函數(shù),而是拋物線,更加符合人眼的視覺特性,而且根據(jù)DCT系數(shù)的頻率分量對宏塊進行分類,計算出的對比度掩蔽因子更加準確,同時通過反復試驗,對參考模型中的一些參數(shù)進行了修正,使模型適用于H.264的離散余弦變換。我們對JND模型的優(yōu)劣進行了仿真測試,結(jié)果表明本文的JND模型性能良好。接著,本文將JND模型應(yīng)用于H.264編碼算法,利用模型計算得到JND閾值來去除DCT系數(shù)中的視覺冗余,實驗結(jié)果表明,添加JND模型之后編碼大約節(jié)省了6%的編碼碼率;同時我們還利用JND閾值對H.264編碼圖像宏塊時使用的編碼模式進行篩選,縮小了模式選擇范圍,降低了編碼計算量,提高了編碼效率。最后,本文利用虛擬機、FFMPEG、Jrtplib以及JND視覺模型搭建了一個仿真的實時視頻傳輸系統(tǒng),測試了系統(tǒng)的實時性;同時,利用大恒相機、FFMPEG、MFC、PC機實現(xiàn)了一個真實的實時視頻傳輸系統(tǒng),詳細描述了系統(tǒng)軟件的編寫,并對軟件的視頻播放、視頻保存、視頻截圖以及視頻翻轉(zhuǎn)等功能進行了測試,測試結(jié)果表明軟件所有功能得到實現(xiàn),系統(tǒng)運作正常。
[Abstract]:With the rapid development of Internet and streaming media technology, real-time video communication technology has made great progress. Today, real-time video transmission systems have been widely used in military command, security monitoring, social networking and other fields [1]. At the same time, the speed of video transmission and the quality of video are increasingly demanding. In order to transmit high quality video image smoothly under limited bandwidth, it is necessary to adopt high performance hardware and efficient coding algorithm. H.264 is a new generation video compression standard suitable for low bit rate video transmission. It has higher coding efficiency, better coding quality and better network adaptability than previous coding standards. In this paper, the coding standard of H.264 is introduced, and some key coding techniques, such as inter-frame prediction, intra-frame prediction, DCT transform and quantization, are introduced, which lays a theoretical foundation for the application of JND model to H.264 coding algorithm. Then, this paper introduces the luminance characteristics, spatial frequency sensitivity and contrast masking characteristics of human eyes. Then, an improved JND model applied to H.264 is proposed according to the JND model proposed by Wei for the JPEG standard. In this paper, the brightness adaptive factor of JND model is not a linear function, but a parabola, which is more in line with the visual characteristics of human eyes, and classifies macroblocks according to the frequency components of DCT coefficients, and the calculated contrast masking factor is more accurate. At the same time, some parameters in the reference model are modified through repeated experiments to make the model suitable for the discrete cosine transform of H. 264. The simulation results show that the JND model has good performance. Then, the JND model is applied to H.264 coding algorithm, and the JND threshold is calculated to remove the visual redundancy in DCT coefficients. The experimental results show that the coding rate is about 6% less after adding JND model. At the same time, the JND threshold is used to filter the coding mode used in H. 264 image macroblock, which reduces the range of mode selection, reduces the amount of coding computation and improves the coding efficiency. Finally, this paper uses virtual machine FFMPEGG Jrtplib and JND vision model to set up a real time video transmission system, and tests the real time of the system. At the same time, a real time video transmission system is realized by using FFMPEGG MFCPC. The system software is described in detail, and the functions of video playing, video saving, video capture and video flipping are tested. The test results show that all the functions of the software have been realized and the system is running normally.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN919.8

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本文編號：2037613