基於遮蔽及色彩資訊之適應性深度視訊編碼

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姓名

林函萱(Han-hsuan Lin) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

基於遮蔽及色彩資訊之適應性深度視訊編碼
(Adaptive)

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摘要(中)

多視角視訊轉播漸成為趨勢，而自由視角雖可提供多視角供使用者選擇觀賞，但合成視角畫面品質仍有改進空間，此可由3D視訊編碼端提升合成畫面品質。因此，本論文提出針對深度視訊編碼，提升合成視角畫面中高複雜度垂直紋理及遮蔽之部分區域品質，其設計共包含兩個部分。從先前的研究成果得知，合成視角的失真除了受深度圖的品質影響，也和色彩視訊的垂直紋理複雜度有關聯性，因此第一部分參考色彩視訊區塊之垂直紋理複雜度，判斷深度視訊之目前區塊是否為易造成合成視角影像失真區域，以決定適用的深度視訊編碼模式決策之RD cost，此部分採用多視角視訊編碼架構MVC (multi-view video coding)。由於立體視覺影像中，遮蔽區域的深度資訊不確定性會影響合成視角畫面品質，因此在第二部分，本論文利用left right check (LRC)找出左右視角所對應的遮蔽圖，再結合由色彩視訊的重建畫面垂直紋理複雜度資訊，將區塊分成不同等級，對rate-distortion optimization (RDO)之RD cost採取不同的Lagrange參數調整，此部分採用3D-HEVC (3D high-efficiency video coding)編碼架構。由修改3D-HEVC之參考軟體HTM6.0的深度視訊編碼器實驗結果顯示，本論文所使用的演算法在第一部份實驗BDBR平均上升0.25%，而BDPSNR平均下降0.01dB，第二部份的實驗結果顯示平均BDBR少0.22%，合成視角的平均BDPSNR下降約0.01dB，而遮蔽區域且垂直紋理複雜度高之區域其合成視角畫面品質提升。

摘要(英)

The tendency of TV service is broadcasting with multi-view videos. Although free-view TV provides optional views to users, there is still the room to improve the quality of color videos synthesized from 3D reconstructed videos. Thus, this thesis proposes adaptive depth video coding to improve the quality of regions which are occluded in the stereopsis and have highly vertical texture in the reconstruct color video. The research is composed of two parts. From the previous research result, we know distortions in synthesized views depend on not only coding distortions in the depth video but also the vertical textures in the reconstructed color video. Consequently, the first part consults the vertical complexity of textures in the reconstructed color video to detect regions with more distortions in the synthesized view. The appropriate RD cost function for depth video coding is selected. The implementation is based on multi-view video coding (MVC). Since the uncertainty of occluded regions in the stereoscopy reduces the quality of synthesized view, this thesis applies left-right check (LRC) based occlusion detection. The information of the occluded map and the vertical texture complexity map is fused to select proper Lagrange parameter in the RD cost function for each largest coding unit (LCU) in the left and right views. The implementation is based on 3D high-efficiency video coding. Our experimental results show that the first part of the proposed scheme has 0.25% BDBR increment and only loss 0.01dB BDPSNR in virtual views compared with the original JMVC 6.0.3. In the experiment of the second part of the proposed scheme, the result shows that the average BDBR decrease is 0.22% and loss of BDPSNR is 0.01dB, with better synthesized viewing quality in the blocks with high vertical complexity and occlusion.

關鍵字(中)

★ 3D high-efficiency video coding (3D-HEVC)
★ multi-view video coding (MVC)
★ 合成視角
★ 遮蔽
★ 垂直紋理複雜度
★ rate-distortion optimization (RDO)

關鍵字(英)

★ 3D high-efficiency video coding (3D-HEVC)
★ multi-view video coding (MVC)
★ synthesized view
★ occlusion
★ vertical texture complexity
★ rate-distortion optimization

論文目次

摘要 i
Abstract ii
目錄 v
圖目錄 vii
表目錄 x
第一章緒論 1
1.1前言 1
1.2研究動機 1
1.3研究方法 2
1.4論文架構 2
第二章 3D High-Efficiency Video Coding 3
2.1 High-Efficiency Video Coding 簡介 3
2.1.1 HEVC視訊編碼器之架構 3
2.1.2 HEVC之預測架構 4
2.1.3 HEVC基本編碼單位 5
2.1.3.1編碼單位(Coding Unit, CU) 6
2.1.3.2預測單位(Prediction Unit, PU) 6
2.1.3.3轉換單位(Transform Unit, TU) 7
2.1.3.4最佳預測模式決策 7
2.2 3D High-Efficiency Video Coding 簡介 9
2.2.1 3D-HEVC編碼架構 9
2.2.2 3D-HEVC深度編碼模式 10
2.2.3 3D-HEVC視角合成演算法 12
2.3總結 13
第三章提升合成畫面品質之深度視訊壓縮演算法現況 14
3.1深度視訊壓縮後處理提升合成畫面品質之文獻探討 14
3.2以高品質合成視角為導向之深度視訊編碼模式決策 15
3.3總結 18
第四章本論文提出之基於遮蔽及色彩資訊之適應性深度視訊編碼方案 19
4.1本論文採用之深度視訊編碼預測架構 19
4.2以色彩視訊資訊提升合成視角品質之RD代價函式設計 20
4.3以遮蔽及色彩視訊資訊適應性提升合成視角品質之設計 23
4.2.1遮蔽區域 23
4.2.2垂直紋理複雜度 30
4.2.3結合以遮蔽及垂直紋理複雜度資訊提升合成視角品質導向的RD cost函式演算法 33
4.4 總結 37
第五章實驗結果與分析 38
5.1實驗環境與參數設定 38
5.2以色彩視訊資訊提升合成視角品質之RD代價函式設計實驗結果分析 42
5.3基於遮蔽及色彩資訊之適應性深度視訊編碼之實驗結果分析 51
5.4總結 61
第六章結論與未來展望 62
參考文獻 63

參考文獻

[1] Y.-S. Ho and K.-J. Oh, “Overview of multi-view video coding,” in Proceedings of IEEE International Workshop on Systems, Signals and Image Processing, pp. 5-12, June 2007.
[2] K. Muller, H. Schwarz, D. Marpe, C. Bartnik, S. Bosse , H. Brust, T. Hinz, H. Lakshman, P. Merkle, F.H. Rhee, G. Tech, M. Winken, and T. Wiegand, “ 3D high-efﬁciency video coding for multi-view video and depth data,” IEEE Transactions on Image Processing, Vol. 22, No. 9, pp. 3366-3378, Sept. 2013.
[3] K.-J. Oh, A. Vetro, and Y.-S. Ho, “Depth coding using a boundary reconstruction filter for 3-D video system,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 21, No. 3, pp. 350-359, Mar. 2011.
[4] V. A. Nguyen, M. Dongbo, and M.N. Do, “ Efficient techniques for depth video compression using weighted mode filtering,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 23, No. 2, pp. 189-202, Feb. 2013.
[5] B. T. Oh, J. Lee, and D.-S. Park, “ Depth map coding based on synthesized view distortion function,” IEEE Journal of Selected Topics in Signal Processing, Vol. 5, No. 7, pp. 1344-1352, Nov. 2011.
[6] 呂志宏，以高品質合成視角為導向之快速深度視訊編碼模式決策，國立中央大學通訊工程學系，碩士論文，民國101年6月。
[7] G. Egnal and R.P. Wildes, “Detecting binocular half-occlusions: empirical comparisons of five approaches,” IEEE Transections on Pattern Analysis and Matching Intelligence, Vol. 25, No. 8, pp. 1127-pp.1133, Nov. 2002.
[8] G.-J. Sullivan, J. Ohm, W.-J. Han, and T. Wiegand,“Overview of the high efficiency video coding (HEVC) standard,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 22, No. 12, pp. 1649 – 1668, Dec. 2012.
[9] H. Schwarz, D. Marpe, and T. Wiegand, “Analysis of hierarchical B pictures and MCTF,” in Proceedings of IEEE International Conference on Multimedia and Expo, pp. 1929-1932, July 2006.
[10] T. Wiegand, G. Sullivan, G. Bjøntegaard, 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.
[11] X. Shen and Y. Lu, “CU splitting early termination based on weighted SVM,” EURASIP Journal on Image and Video Processing, Vol. 2013, No. 1,pp. 1-11, Dec. 2013.
[12] ISO/IEC JTC1/SC29/WG11, “High Efficiency Video Coding (HEVC) Test Model 10 (HM10) Encoder Description,” Doc JCTVC-L1002_v3, Geneva, Jan. 2013.
[13] ISO/IEC JTC1/SC29/WG11, “3D-HEVC Test Model 3,” Doc JCT3V-C1005_d0, Geneva, Jan. 2013.
[14] K. Muller, P. Merkle, G. Tech ,and T. Wiegand, “3D video coding with depth modeling modes and view synthesis optimization,” in Proceedings of Signal & Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1-4, Hollywood, Canada, Dec. 2012.
[15] W. Hu, O. C. Au, L. Sun, W. Sun, L. Xu, and Y. Li, “Adaptive depth map filter for blocking artifact removal and edge preserving,” in Proceedings of IEEE International Symposium on Circuits and Systems, pp. 369-372, Seoul, May 2012.
[16] D. De Silva, W. Fernando, H. Kodikaraarachchi, S. Worrall, and A. Kondoz, “A depth map post-processing framework for 3D-TV systems based on compression artifact analysis,” IEEE Journal of Selected Topics in Signal Processing, No. 99, Aug. 2011.
[17] V. A. Nguyen, D. Min, and M.N. Do, “ Efficient techniques for depth video compression using weighted mode filtering,” IEEE Transactions on Circuits System for Video Technology, Vol. 23, No. 2, pp. 189-202, Feb. 2013.
[18] C. Lee and Y.-S. Ho, “ View synthesis using depth map for 3D video,” in Proceedings of Asia-Pacific Signal and Information Processing Association on Annual Summit and Conference, pp. 350-357, Oct. 2009.
[19] G. Cernigliaro, M. Naccari, F. Jaureguizar, J. Cabrera, and N. Garcia, “Depth perceptual video coding for free viewpoint video based on H.264/AVC,” in Proceedings of Picture Coding Symposium (PCS), pp. 153-156, Krakow, May 2012.
[20] S. Ince and J. Konrad, “Geometry-based estimation of occlusions from video frame pairs,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 2, pp.933-936, March 2005.
[21] C.-J. Tsai, C.-W. Tang, C.-H. Chen, and Y.-H. Yu, “Adaptive rate-distortion optimization using perceptual hint,” IEEE International Conference on Multimedia and Expo, Vol. 1, pp. 667-670, Taipei, June 2004.
[22] ISO/IEC JTC1/SC29/WG11 and ITU-T SG16 Q.6, “JMVC 1.0 software,” JVT-AA212, Geneva, April 2008.
[23] ISO/IEC MPEG & ITU-T VCEG, “Multi-view Video plus Depth (MVD) Format for Advanced 3D Video System,” Doc. JVT-W-100, April 2007.
[24] ISO/IEC JTC1/SC29/WG11, “View synthesis algorithm in view synthesis reference software 2.0(VSRS2.0),” Doc. M16090, Feb. 2009.
[25] ISO/IEC JTC1/SC29/WG11, “Common Test Conditions of 3DV Core Experiments,” Doc. JCT3V-C1100, Geneva, Jan. 2013.
[26] Z. Peng, M. Yu, G. Jiang, Y. Si, and F. Chen, “Virtual view synthesis oriented fast depth video encoding algorithm,” in Proceedings of IEEE International Conference on Industrial and Information System, Vol. 1, pp. 204-207, Dalian, July 2010.
[27] G. Bjontegaard, “Calculation of average PSNR difference between RD-curves,” ITU-T Q6/SG16, Doc. VCEG-M33, April 2001.
[28] ISO/IEC JTC1/SC29/WG11, “JCT-3V AHG Report: MV-HEVC and 3D-HEVC Software Integration (AHG5),” Doc. JCT3V-H0005, Valencia, March 2014.

指導教授

唐之瑋(Chih-wei Tang)

審核日期

2014-7-18

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