請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47820完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳宏銘(Homer Chen) | |
| dc.contributor.author | Tao-Sheng Ou | en |
| dc.contributor.author | 歐道聖 | zh_TW |
| dc.date.accessioned | 2021-06-15T06:20:31Z | - |
| dc.date.available | 2013-08-12 | |
| dc.date.copyright | 2010-08-12 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-08-10 | |
| dc.identifier.citation | [1] T. Chiang and Y.-Q. Zhang, “A new rate control scheme using quadratic rate distortion model,” IEEE Trans. Circuits Syst. Video Technol., vol.7, no.1, pp.246-250, Feb. 1997.
[2] H.-J. Lee, T. Chiang, and Y.-Q. Zhang, “Scalable Rate Control for MPEG-4 Video,” IEEE Trans. Circuits Syst. Video Technol., vol.10, no.6, pp.878-894, Sept. 2000. [3] ¬N. Kamaci, Y. Altunbasak, and R. M. Mersereau, “Frame bit allocation for the H.264/AVC video coder via Cauchy-density-based rate and distortion models,” IEEE Trans. Circuits Syst. Video Technol., vol. 15, no. 8, Aug. 2005. [4] S. Ma, W. Gao, and Y. Lu, “Rate-distortion analysis for H.264/AVC video coding and its application to rate control,” IEEE Trans. Circuits Syst. Video Technol., vol. 15, no. 12, pp. 1533-1544, Dec. 2005. [5] Z. He, Y.-K. Kim, and S. K. Mitra, “Low-delay rate control for DCT video coding via ρ-domain source modeling,” IEEE Trans Circuits Syst. Video Technol., vol. 11, no. 8, Aug. 2001. [6] Wu Yuan, S. Lin, Y. Zhang, W. Yuan, H. Luo, “Optimum bit allocation and rate control for H.264/AVC,” IEEE Trans. Circuits Syst. Video Technol., vol.16, no.6, pp. 705-715, Jun. 2006. [7] D.-K. Kwon, M.-Y. Shen, and C.-C. Jay Kuo, “Rate control for H.264 video with enhanced rate and distortion models,” IEEE Trans. Circuits Syst. Video Technol., vol. 17, no. 5, May 2007. [8] C. An and T. Q. Nguyen, “Iterative rate-distortion optimization of H.264 with constant bit rate constraint,” IEEE Trans. Image Process., vol. 17, no. 9, pp.1605-1615, Sept. 2008. [9] Z. He and S. K. Mitra, “Optimum bit allocation and accurate rate control for video coding via ρ-domain source modeling,” IEEE Trans. Circuits Syst. Video Technol., vol. 12, no. 10, pp. 840–849, Oct. 2002. [10] Z. Chen and K. N. Ngan, “Recent advances in rate control for video coding,” Signal Process: Image Commun., vol. 22, no. 1, pp. 19-38, Jan. 2007. [11] Draft ITU-T Recommendation and Final Draft International Standard of Joint Video Specification (ITU-T Rec. H.264 | ISO/IEC 14496-10 AVC), May 2003. [12] T. Wiegand, G. J. Sullivan, G. Bjontegaard, and A. Luthra, “Overview of H.264 video coding standard,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 560-576, Jul. 2003. [13] D. M. Chandler and S. S. Hemami, “VSNR: a wavelet-based visual signal-to-noise ratio for natural images,” IEEE Trans. Image Process., vol.16, no.9, pp.2284-2298, Sept. 2007. [14] H. R. Sheikh and A. C. Bovik, “Image information and visual quality,” IEEE Trans. Image Process., vol.15, no.2, pp.430-444, Feb. 2006. [15] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600-612, April 2004. [16] Z. Wang, L. Lu, and A. C. Bovik, “Video quality assessment based on structural distortion measurement,” Signal Processing: Image Communication, special issue on “Objective video quality metrics”, vol. 19, no. 2, pp. 121-132, Feb. 2004. [17] H.R. Sheikh, M.F. Sabir, and A.C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms,” IEEE Trans. Image Process., vol.15, no.11, pp.3440-3451, Nov. 2006. [18] G.-H. Chen, C.-L. Yang, and S.-L. Xie, “Gradient-based structural similarity for image quality assessment,” in Proc. IEEE int. Conf. on Image Process., pp. 2929-2932, Oct. 2006. [19] B. Wang, Z. Wang, Y. Liao, and X. Lin, “HVS-based structural similarity for image quality assessment”, Int. Conf. on Signal Processing, pp. 1194-1197, Oct. 2008. [20] C. Li and A. C. Bovik, “Three-Component Weighted Structural Similarity Index,” SPIE Conf. on Image Quality and System Performance, January 19-22, 2009. [21] S.S. Channappayya, A.C. Bovik, C. Caramanis and R.W. Heath, “Design of linear equalizers optimized for the structural similarity index,” IEEE Trans. on Image Processing, vol. 17, no. 6, pp. 857-872, June 2008. [22] Z. Wang, Q. Li, and X. Shang, “Perceptual Image Coding Based on a Maximum of Minimal Structural Similarity Criterion,” in Proc. IEEE Int. Conf. Image Process., vol.2, pp. 121-124, Sept. 2007. [23] T. Richter, K. J. Kim, “A MS-SSIM optimal JPEG 2000 encoder,” in Proc. Data Compression Conf., pp.401-410, Mar. 2009. [24] A. C. Brooks, X. Zhao, and T. N. Pappas, “Structural Similarity Quality Metrics in a Coding Context: Exploring the Space of Realistic Distortions,” IEEE Trans. Image Process., vol. 17, no. 8, pp. 1261-1273, Aug. 2008. [25] S.S. Channappayya, A.C. Bovik, R.W. Heath, “Rate Bounds on SSIM Index of Quantized Images,” IEEE Trans. Image Process., vol.17, no.9, pp.1624-1639, Sept. 2008. [26] Z. Y. Mai, C. L. Yang, K. Z. Kuang and L. M. Po, 'A Novel Motion Estimation Method based on Structural Similarity for H.264 Inter Prediction,” in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, vol. 2, pp. 913-916, May 2006. [27] Z.-Y. Mai, C.-L. Yang, L.-M. Po, and S.-L. Xie, “A new rate-distortion optimization using structural information in H.264 I-frame encoder,” Lecture Notes In Computer Science, pp. 435-441, 2005. [28] L. Teixeira and L. Corte-Real, “H.264 rate-distortion analysis using subjective quality metric,” Proc. the 2nd Int. Workshop on Future Multimedia Networking, pp. 248-253, 2009. [29] Xiaonan Zhao et al., “Structural texture similarity metrics for retrieval applications,” in Proc. IEEE int. Conf. on Image Processing, pp. 1196-1199, Oct. 2008. [30] A.C. Brooks and T.N. Pappas, “Using structural similarity quality metrics to evaluate image compression techniques,” in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, vol. 1, pp. 873-876, Apr. 2007. [31] C.-L. Yang, H.-X. Wang, and L.-M. Po, “Improved inter prediction based on structural similarity in H.264,” in Proc. IEEE Int. Conf. Signal Process. and Communications, pp. 340-343, Nov. 2007. [32] S.S. Channappayya, A.C. Bovik, C. Caramanis, and R.W. Heath, “SSIM-optimal linear image restoration,” in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, pp. 765-768, Mar. 2008. [33] J. Zujovic, T.N. Pappas, and D.L. Neuhoff, “Structural similarity metrics for texture analysis and retrieval,” in Proc. IEEE int. Conf. on Image Processing, pp. 2225-2228, Nov. 2009. [34] S. Wang, S. Ma, W. Gao, “SSIM based perceptual distortion rate optimization coding,” in Proc. of SPIE Vol. 7744 Visual Communication and Image Processing, 774407, 1-8, Huang Shan, China, Jul. 2010. [35] JVT reference software [Online]. Available: http://bs.hhi.de/~suehring/ tml/download/. [36] D.M. Rouse and S.S. Hemami, “Understanding and simplifying the structural similarity metric,” in Proc. IEEE Int. Conf. Image Process., pp. 1188-1191, Oct. 2008. [37] K. Seshadrinathan and A.C. Bovik, “Unifying analysis of full reference image quality assessment,” in Proc. IEEE Int. Conf. Image Process., pp. 1200-1203, Oct. 2008. [38] T. Wiegand et al., “Rate-constrained coder control and comparison of video coding standards,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 688-703, Jul. 2003. [39] A. Ortega and K. Ramchandran, “Rate-distortion method for image and video compression” IEEE Signal Processing Magazine, pp. 23-50, Nov. 1998. [40] G. J. Sullivan and T. Wiegand, “Rate-distortion optimization for video compression,” IEEE Signal Processing Magazine, pp. 74-90, Nov. 1998. [41] Z. Wang and A.C. Bovik, “Mean squared error: Love it or leave it? - A new look at signal fidelity measures”, IEEE Signal Processing Magazine, vol. 26, no. 1, pp 98-117, Jan. 2009. [42] Y.-H. Huang, T.-S. Ou, and H. H. Chen, “Perceptual rate-distortion optimization using structural similarity index as quality metric,” submitted to IEEE Trans Circuits Syst. Video Technol. [43] P.-Y. Su, Y.-H. Huang, T.-S. Ou, and H. H. Chen, “Predictive Lagrange multiplier selection for perceptual-based rate-distortion optimization,” Proc. the 5th Int. Workshop on Video Processing and Quality Metrics for Consumer Electronics (VPQM), Jan. 2010. [44] Y.-H. Huang, T.-S. Ou, and H. H. Chen, “Perceptual-based coding mode decision,” in Proc. IEEE Int. Symp. Circuits and Systems, pp. 393-396, May 2010. [45] MPEG-2 Test Model 5, Doc. ISO/IEC JTC1/SC29/WG11/93-400, Sydney, Australia, Apr. 1993. [46] Video Coding for Low Bit Rate Communication, ITU-T Rec. H.263, Nov. 1995. [47] MPEG-4 Video Verification Model v8.0, ISO/IEC JTC1/SC29/WG11 Coding of Moving Pictures and Associated Audio MPEG97/N1796, Stockholm, Sweden, Jul. 1997. [48] Z. G. Li, F. Pan, K. P. Lim, and S. Rahardja, “Adaptive rate control for H.264,” in Proc. IEEE Int. Conf. Image Process., pp. 745–748, Oct. 2004. [49] Z. G. Li, F. Pan, K. P. Lim, G. N. Feng, X. Lin, and S. Rahardaj, Adaptive basic unit layer rate control for JVT, Joint Video Team of ISO/IEC JTC1/SC29/WG11 and ITU-T SG16/Q.6 Doc. JVT-G012, Pattaya, Thailand, Mar. 2003. [50] J. Dong and N. Ling, “A context-adaptive prediction scheme for parameter estimation in H.264/AVC macroblock layer rate control,” IEEE Trans. Circuits Syst. Video Technol., vol. 19, no. 8, Aug. 2009. [51] X. Yang, W. Lin, Z. Lu, X. Lin, S. Rahardja, E. Ong, and S. Yao, “Rate control for videophone using local perceptual cues,” IEEE Trans. Circuits Syst. Video Technol., vol. 15, no. 4, Apr. 2005. [52] Y. Liu, Z. G. Li, and Y. C. Soh, “Region-of-interest based resource allocation for conversational video communication of H.264/AVC,” IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 1, Jan. 2008. [53] H. Li, Z. Wang, H. Cui, and K. Tang, “An improved ROI-based rate control algorithm for H.264/AVC,” Int. Conf. on Signal Processing, vol.2, pp. 16-20, 2006. [54] C.-W. Tang, C.-H. Chen, Y.-H. Yu, and C.-J. Tsai, “Visual sensitivity guided bit allocation for video coding,” IEEE Trans. Circuits Syst. Video Technol., vol. 8, no. 1, Feb. 2006. [55] C.-W. Tang, “Spatiotemporal visual considerations for video coding,” IEEE Trans. Circuits Syst. Video Technol., vol. 9, no. 2, Feb. 2007. [56] H. Yu, F. Pan, Z. Lin, and Y. Sun, “A perceptual bit allocation scheme for H.264,” in Proc. IEEE Int. Conf. Multimedia and Expo, Jul. 2005. [57] Z. Liu, H. Yan, L. Shen, Y. Wang, Z. Zhang, “A motion attention model based rate control algorithm for H.264/AVC,” in Proc. IEEE/ACIS Int. Conf. Computer and Information Science, pp.568-573, Jun. 2009. [58] T.-H. Wu, G.-L. Wu, and S.-Y. Chien, “Bio-inspired perceptual video encoding based on H.264/AVC,” in Proc. IEEE Int. Symp. Circuits and Systems, pp.2826-2829, May 2009. [59] Z. Chen and C. Guillemot, “Perceptually-friendly H.264/AVC video coding based on foveated just-noticeable-distortion model,” IEEE Trans. Circuits Syst. Video Technol., 2010. [60] S. Lee, M. S. Pattichis, and A. C. Bovik, “Foveated video compression with optimal rate control,” IEEE Trans. Image Process., vol. 10, no. 7, Jul. 2001. [61] Z. Wang, L. Lu, and A. C. Bovik, “Foveation scalable video coding with automatic fixation selection,” IEEE Trans. Image Process., vol. 12, no. 2, Feb. 2003. [62] J. L. Devore, and N. R. Farnum, Applied Statistics for Engineers and Scientists. New York: Duxbury, 1999. [63] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004. [64] G. Bjontegaard, “Calculation of average PSNR differences between RD-curves,” VCEG-M33, Apr. 2001. [65] Performance evaluation of perceptual-based bit allocation and rate control scheme [Online]. Available: http://mpac.ee.ntu.edu.tw/~odeson/projects/ssimrc/. [66] T.-S. Ou, Y.-H. Huang, and H. H. Chen, “A perceptual-based approach to bit allocation for H.264 encoder,” in Proc. of SPIE Vol. 7744 Visual Communication and Image Processing, 77441B, 1-10, Huang Shan, China, Jul. 2010. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47820 | - |
| dc.description.abstract | 編碼後的影像品質最終是由人眼判斷的。然而,現今大部分最佳化位元率控制方法使用的誤差衡量標準,是和人眼感知不全然相關的平均方差及其類似的誤差衡量標準。雖然已經有將人眼視覺特性納入考慮所設計的位元率控制方法被開發出來,但其中絕大多數的方法在設計上未考慮位元率和誤差的最佳化。
在本論文中,我們使用結構相似性為影像品質的衡量標準,提出了一個位元率誤差模型,並基於此模型,開發了用於H.264的編碼器的最佳化位元分配與位元率控制演算法。所提出的方法相較於H.264的參考軟體JM,最多可以達到25%的位元率減少。我們更進一步將所提出的位元率控制演算法與基於視覺特性的模式決策方法整合,相較於JM,整合後的方法可以達到32%的位元率減少。主觀的衡量也顯示了我們所提出的位元率控制方法提供了更好的影像品質。 | zh_TW |
| dc.description.abstract | The quality of encoded video is ultimately judged by human eyes; however, most previous optimum rate control schemes use mean squared error and the like as quality metrics that are poorly correlated with human perception. Although perceptual-based rate control incorporating the characteristics of human visual system has been developed, most existing schemes do not take rate-distortion optimization into consideration. In this thesis, we use the structural similarity index as the quality metric for rate-distortion modeling and develop an optimum bit allocation and rate control scheme for H.264. This scheme achieves up to 25% bit-rate reduction over the JM reference software of H.264. We also show that the proposed scheme can be further integrated with the perceptual-based mode decision scheme under the rate-distortion optimization framework to bring the bit-rate reduction over the JM reference software to 32%. The perceptual quality improvement is examined by subjective inspection. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T06:20:31Z (GMT). No. of bitstreams: 1 ntu-99-R97942057-1.pdf: 3584848 bytes, checksum: 1289fa7bf17f18f5abd2fadf665a4e95 (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES ix Chapter 1 Introduction 1 1.1 Video Coding Systems 1 1.2 Rate Control for Video Coding 2 1.3 Research Contribution 2 1.4 Thesis Organization 3 Chapter 2 Preliminary 4 2.1 Overview of H.264 Video Coding Standard 4 2.2 Structural Similarity Index 5 2.3 Perceptual-Based Rate-Distortion Optimization 8 Chapter 3 Related Work 11 3.1 Rate Control for H.264 Encoder 11 3.2 Perceptual-Based Bit Allocation and Rate Control 12 Chapter 4 Perceptual-Based Rate Control 14 4.1 Rate-Distortion Modeling Using SSIM as Quality Metric 14 4.2 Optimum Bit Allocation Using SSIM as Quality Metric 17 4.3 Proposed Rate Control Scheme 19 4.3.1 Key Frame 19 4.3.2 Target Bit Budget Determination 20 4.3.3 QP Determination 21 4.3.4 Model Update 22 4.3.5 Integration with the Perceptual-Based RDO 23 4.4 Experimental Results 24 4.4.1 Experimental Settings 24 4.4.2 Evaluation of R-D Performance 41 4.4.3 Computational Overhead 43 4.4.4 Subjective Inspection 51 Chapter 5 Conclusion 52 REFERENCE 53 CURRICULUM VITAE 61 | |
| dc.language.iso | en | |
| dc.subject | 結構相似性 | zh_TW |
| dc.subject | H.264/AVC | zh_TW |
| dc.subject | R-D優化 | zh_TW |
| dc.subject | 位元率控制 | zh_TW |
| dc.subject | H.264/AVC | en |
| dc.subject | structural similarity | en |
| dc.subject | rate control | en |
| dc.subject | rate-distortion optimization | en |
| dc.title | 基於視覺特性之影像編碼位元率控制 | zh_TW |
| dc.title | Perceptual-Based Rate Control for Video Coding | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 王家祥(Jia-Shung Wang),黃寶儀(Polly Huang),鐘國亮 | |
| dc.subject.keyword | H.264/AVC,R-D優化,位元率控制,結構相似性, | zh_TW |
| dc.subject.keyword | H.264/AVC,rate-distortion optimization,rate control,structural similarity, | en |
| dc.relation.page | 62 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2010-08-10 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位: | 電信工程學研究所 | |
文件中的檔案:
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-99-1.pdf 未授權公開取用 | 3.5 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。
