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研究生:李名軒
研究生(外文):Mian-Shiuan Li
論文名稱:利用人眼視覺特性與幾何預測於視訊編碼之高效率區塊模式決策與效能提升
論文名稱(外文):Efficient Mode Decision and Performance Improvement based on Human Visual System and Geometric Prediction for Video Coding
指導教授:陳美娟陳美娟引用關係
指導教授(外文):Mei-Juan Chen
學位類別:碩士
校院名稱:國立東華大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
論文頁數:110
中文關鍵詞:區塊模式決策H.264/AVC人眼可辨視覺差異特性位元失真成本高斯函數多視角視訊編碼視角間視差向量預測幾何關係視差
外文關鍵詞:Mode decisionH.264/AVCJust-Noticeable-DifferenceRate-Distortion CostGaussian DistributionMulti-view Video CodingDisparity Vector PredictionGeometry of PositionParallax
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隨著科技的日益進步,生活品質隨之提升,人們對於網路多媒體的需求也愈來愈高,尤其在視訊影像傳輸的應用,更是與生活息息相關,於是視訊影像的相關研究就顯得格外重要。本研究論文針對視訊影像的壓縮處理做深入的研究和探討,以提升視訊影像傳輸之品質與效能。此論文將分為兩個部分,第一個部分基於人眼可辨視覺差異特性與位元失真成本關係提出應用在H.264/AVC的高效率區塊模式決策演算法。首先,我們利用高斯函數來建構位元失真成本與人眼可辨視覺差異特性的關係模型,以此模型與巨區塊之位元失真成本做比較,做為是否減少候選區塊模式之數量的判斷條件,以此達到區塊模式決策提前終止的效果。人眼可辨視覺差異特性除了與位元失真成本的關係外,我們也用來與畫面殘餘值和畫面內容一同考量,做為分析巨區塊內容的水平垂直分割方式。根據實驗結果,我們可以達到很高的編碼時間節省率,並且在資料量與客觀、主觀品質上都維持不錯的表現。此論文的第二個部分在多視角視訊編碼上提出了一個新的視角間視差向量預測演算法,在此我們同樣利用了人眼可辨視覺差異特性來思考視角間視差向量預測。有別於傳統利用鄰近區域之移動向量的視角間視差向量預測,我們利用了各視角相機位置的幾何關係,運算出在不同視角間的視差關係,並且以此做為視角間視差向量預測的基礎,最後再經由高斯函數的加權運算得到最後的視角間視差的預測向量。實驗結果可以證明我們提出此新的視角間視差向量預測演算法有效的達到更好地資料量縮減與主觀與客觀品質。
With increasingly sophisticated technology, multimedia communication is more and more important for humans. Video transmission and communication is assimilated into our life. For this reason, the study with regard to video and image becomes more significant. This thesis focuses on the video compression for the improvement of video quality and transmission efficiency and mainly consists of two parts. The first part of this thesis proposes an efficient mode decision algorithm based on the correlation of Just-Noticeable-Difference (JND) and Rate-Distortion (RD) cost to reduce the computational complexity of H.264/AVC. First, the relationship between the average RD cost and the number of JND pixels is established by Gaussian distributions. Thus, we can compare the RD cost of the current 16x16 mode with the predicted thresholds from these models for rapid mode selection. In addition, we use the JND visual model and the residual data and image content for Horizontal/Vertical (H/V) detection, and then utilize the result to predict the partition in a macroblock (MB). From the experimental results, a greater time saving can be achieved. And the proposed algorithm maintains performance and quality effectively. In the second part of this thesis, an accurate disparity vector prediction algorithm for multi-view video coding (MVC) is proposed. Differing from traditional disparity vector prediction by using the information of motion vectors of neighboring blocks, the geometry of camera position is utilized to calculate the parallax of different viewpoints. This parallax is the foundation of disparity vector prediction (DVP). We also apply Just-Noticeable-Difference (JND) human visual model to consider the DVP. Filtering by Gaussian function, the geometric DVP can be obtained. According to our experimental results, the significant data reduction and subjective/objective quality enhancement can be achieved.
Chapter 1 Introduction………………………………………….…………………...1
1.1 Overview of Video Coding………………………………………………………1
1.1-1 Motion Estimation and Compensation……………………………………...2
1.1-2 Inter Mode…………………………………………………………………..4
1.1-3 Intra Mode…………………………………………………………………..5
1.1-4 Discrete Cosine Transform………………………………………………….7
1.1-5 Quantization………………………………………………………………...8
1.1-6 Entropy Coding……………………………………………………………..9
1.1-7 Deblocking Filter……………………………………………………………9
1.2 Overview of Multi-view Video Coding………………………………………...11
1.3 Motivation………………………………………………………………………13
1.4 Organization of the Thesis……………………………………………………...15
Chapter 2 Overview of Previous Work………………..…………………..………17
2.1 Human Visual Characteristic……………………………………………………17
2.2 Overview of Mode decision…………………………………………………….21
2.2-1 Mode Decision in General Coding………………………………………...21
2.2-2 Related Work of Efficient Mode Decision Algorithm……………………21
2.3 Overview of Disparity Vector Prediction……………………………………….25
2.3-1 Disparity Vector Prediction in Multi-view Video Coding………………...25
2.3-2 Related Works of Disparity Prediction……………………..……………..27
Chapter 3 Proposed Efficient Mode Decision Algorithm………...………………29
3.1 Human Visual Characteristic of a MB………………………………………….29
3.2 Construction of Human Visual Characteristic………………………………….33
3.3 Selection of Intra Mode…………………………………………………………35
3.4 Model of Rate Distortion Cost of Inter Mode …………………………………..39
3.5 Inter Mode Decision based on Model of Rate Distortion Cost……………….51
3.6 Characteristics of Image Direction……………………………………………55
3.7 Entire Algorithm………………………………………………………………59
3.8 Experimental Results of Efficient Mode Decision Algorithm………………….61
Chapter 4 Proposed Disparity Vector Prediction Algorithm……….……………85
4.1 Projective Geometry in Three-Dimensional Coordinate……………………….85
4.2 Disparity Vector Prediction Algorithm…………………………………………89
4.3 Experimental Results of Disparity Vector Prediction Algorithm………………93
Chapter 5 Conclusion and Future Work………………………………………...103
[1] T. Wiegand, G. J. Sullivan, G. Bjontegaard, and A. Luthra, ”Overview of the H.264/AVC video coding standard,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, no. 7, pp. 560-576, July 2003.
[2] M. Tanimoto, “Overview of FTV (free-viewpoint television),” in Proceedings of IEEE International Conference on Multimedia and Expo, pp. 1552–1553, August 2009.
[3] P. Benzie, J. Watson, P. Surman, I. Rakkolainen, K. Hopf, H. Urey, V. Sainov, and C. von Kopylow, “A survey of 3DTV displays: techniques and technologies,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, no.11, pp.1647–1658, November 2007.
[4] A. Smolic, K. Mueller, N. Stefanoski, J. Ostermann, A. Gotchev, G. B. Akar, G. Triantafyllidis, and A. Koz, “Coding algorithms for 3DTV-A survey,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, no. 11, pp.1606-1621, November 2007.
[5] Y. Chen, Y. K. Wang, K. Ugur, M. M. Hannuksela, J. Lainema, and M. Gabbouj, “The emerging MVC standard for 3D video services,” EURASIP Journal on Advances in Signal Processing, vol. 2009, no. 1, January 2009.
[6] C. H. Chou and Y. C. Li, “A perceptually tuned subband image coder based on the measure of just-noticeable-distortion profile,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 5, no. 6, pp. 467-476, December 1995.
[7] C. Grecos and M. Y. Yang, “Fast inter mode prediction for P slices in the H.264 video coding standard,” IEEE Transaction on Broadcasting, vol. 51, no. 2, pp. 256-263, June 2005.
[8] Y. H. Lin and J. L. Wu, “A depth information based fast mode decision algorithm for color plus depth-map 3D videos,” IEEE Transaction on Broadcasting, vol. 57, no. 2, pp. 542-550, June 2011.
[9] M. E. Eduardo, J. M. Amaya, and D. M. Fernando, “An adaptive algorithm for fast inter mode decision in the H.264/AVC video coding standard,” IEEE Transactions on Consumer Electronics, vol. 56, no. 2, pp. 826-834, May 2010.
[10] T. Zhao, H. Wang, S. Kwong, and C.-C. J. Kuo, “Fast mode decision based on mode adaptation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 20, no. 5, pp. 697-705, May 2010.
[11] S. H. Ri., Y. Vatis, and J. Ostermann, “Fast inter-mode decision in an H.264/AVC encoder using mode and lagrangian cost correlation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 19, no. 2, pp. 302-306, February 2009.
[12] T. Gan and P. R. Alface, “Fast mode decision for H.264/AVC encoding of tunnel surveillance video,” in Proceedings of 2010 Second International Conferences on Advances in Multimedia, pp. 7-12, June 2010.
[13] P. H. Chen, H.M. Chen, M.C. Shie, C. H. Su, W. L. Mao, and C. K. Huang, “Adaptive fast block mode decision algorithm for H.264/AVC,” in Proceedings of 5th IEEE Conference on Industrial Electronics and Applications, pp. 2002-2007, June 2010.
[14] H. Tang and H. Shi, “Fast mode decision algorithm for H.264/AVC based on all-zero blocks predetermination,” in Proceedings of International Conference on Measuring Technology and Mechatronics Automation, vol. 2, pp. 780-783, Apr. 2009.
[15] H. Wang, S. Kwong, and C.W. Kok, “An efficient mode decision algorithm for H.264/AVC encoding optimization,” IEEE Transactions on Multimedia, vol. 9, no. 4, pp. 882-888, June 2007.
[16] H. Zeng, C. Cai, and K. K. Ma, “Fast mode decision for H.264/AVC based on macroblock motion activity,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 19, no. 4, pp. 491-499, April 2009.
[17] M. S. Li and M. J. Chen, “Fast HVS-based mode decision for H.264/AVC using just-noticeable-difference,” in Proceedings of International Conference on Image Processing, Computer Vision, and Pattern Recognition, July 2011.
[18] H. Wang, X. Qian, and G. Liu, “Inter mode decision based on just noticeable difference profile,” in Proceedings of 17th IEEE International Conference on Image Processing, pp. 297-300, September 2010.
[19] M. Shafique, B. Molkenthin, and J. Henkel, “An HVS-based adaptive computational complexity reduction scheme for H.264/AVC video encoder using prognostic early mode exclusion,” in Proceedings of Europe Conference & Exhibition Design, Automation & Test, pp. 1713-1718, March 2010.
[20] B.W. Micallef, C.J. Debono, and R.A. Farrugia, “Fast disparity estimation for multi-view plus depth video coding,” in Proceedings of IEEE Visual Communications and Image Processing, November 2011.
[21] B.W. Micallef, C.J. Debono, and R.A. Farrugia, “Exploiting depth information for fast motion and disparity estimation in multi-view video coding,” Transmission and Display of 3D Video, May 2011.
[22] S. Xing, C. Hua, J.G. Lou, and J. Li, “Multiview Image Coding Based on Geometric Prediction,” IEEE Transactions on Circuits and Systems for Video Technology, vol.17, no.11, pp.1536-1548, November 2007.
[23] B.W. Micallef, C.J. Debono, and R.A. Farrugia, “Exploiting depth information for efficient multi-view video coding,” in Proceedings of IEEE International Conference on Multimedia and Expo, pp. 1-6, July 2011.
[24] W. Zhu, X. Tian, F. Zhou, and Y. Chen, “Fast disparity estimation using spatio-temporal correlation of disparity field for multiview video coding,” IEEE Transactions on Consumer Electronics, vol.56, no.2, pp.957-964, May 2010.
[25] Z. Zhang, “Determining the epipolar geometry and its uncertainty : A review.” International Journal of Computer, vol. 27, no. 2 , pp. 161-195, 1998.
[26] H. Richard and Z. Andrew, “Multiple view geometry in computer vision, the second edition,” Cambridge University Press, March 2004.
[27] MSR multi-view sequences [Online]. Available: http://research.microsoft.com/en-us/um/people/sbkang/3dvideodownload/
[28] ISO/IEC JTC1/SC29/WG11, “1D parallel test sequences for MPEG-FTV,” M15378, April 2008.
[29] H.264/AVC reference softwares [Online]. Available: http://iphome.hhi.de/suehring/tml/
[30] G. Bjontegaard, “Calculation of average PSNR differences between RD-curves,” ITU-T SG16 Doc. VCEG-M33. April 2001.
[31] Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, April 2004.
[32] ISO/IEC MPEG & ITU-T VCEG, “Joint multi-view video coding model (JMVC8.5),” JVT-AE207, March 2011.
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