結合內容相關位元率量化模型與興趣區域之H.264/AVC畫面內預測編碼

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：15

、訪客IP：18.191.44.139

姓名

許惠君(Hui-chun Hsu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

結合內容相關位元率量化模型與興趣區域之H.264/AVC畫面內預測編碼
(A Joint Content Adaptive Rate-Quantization Model and Region of Interest Intra Coding of H.264/AVC)

相關論文

★ 基於QT之跨平台無線心率分析系統實現	★ 網路電話之額外訊息傳輸機制
★ 針對與運動比賽精彩畫面相關串場效果之偵測	★ 植基於向量量化之視訊/影像內容驗證技術
★ 植基於串場效果偵測與內容分析之棒球比賽精華擷取系統	★ 以視覺特徵擷取為基礎之影像視訊內容認證技術
★ 使用動態背景補償以偵測與追蹤移動監控畫面之前景物	★ 應用於H.264/AVC視訊內容認證之適應式數位浮水印
★ 棒球比賽精華片段擷取分類系統	★ 利用H.264/AVC特徵之多攝影機即時追蹤系統
★ 利用隱式型態模式之高速公路前車偵測機制	★ 基於時間域與空間域特徵擷取之影片複製偵測機制
★ 結合數位浮水印與興趣區域位元率控制之車行視訊編碼	★ 應用於數位智權管理之H.264/AVC視訊加解密暨數位浮水印機制
★ 基於文字與主播偵測之新聞視訊分析系統	★ 植基於數位浮水印之H.264/AVC視訊內容驗證機制

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

本論文提出一個結合內容相關位元率量化模型與興趣區域之H.264/AVC畫面內預測編碼。在視訊編碼的位元率控制中，畫面內預測編碼佔了很重要的地位，其過高或過低的位元率將影響整體的編碼效率。在本篇論文中，我們提出了一個較準確的畫面內預測位元率量化模型(Rate-Quantization model, R-Q model)。我們根據區塊內容的複雜度與目標之位元率，求得此區塊之編碼率與量化參數(Quantization Parameter, QP)值的相對應關係。此外，在有了此內容相關位元率量化模型後，我們可結合興趣區域編碼，使得本機制在有限的位元率下，於興趣區域給予較多位元數，也就是較低之QP值，而使其具有較佳的畫質，而在視覺較不注意之區域給予較少位元數，藉由內容相關位元率量化模組與興趣區域的結合能在有限之位元數下達到較佳的人眼視覺效果。我們將畫面分成三個區域以各自給予適當的QP值。我們的實驗顯示，整體畫面平均之峰值訊號雜訊比 (Peak Signal to Noise Ratio, PSNR) 雖下降0.27 dB，但人眼視覺最關注區域之PSNR值增加了1.2 dB，而人眼最關注的前兩個區域則增加了0.51 dB。與傳統畫面階層之R-Q model相較，此區塊階層之R-Q model更具彈性，更容易達到目標位元率。

摘要(英)

This thesis presents a joint content adaptive rate-quantization model and region of interest intra coding of H.264/AVC. The rate control of video coding is an important issue and the intra coding plays a very crucial role. Inappropriate assignment of bitrates in intra coding will deteriorate the overall coding performance. We will first present a more accurate content adaptive Rate-Quantization (R-Q) model, by which we can obtain the relationship between the Quantization Parameter (QP) of a macroblock and the block complexity. Given a target bit-rate, we can thus assign a more suitable QP for a frame. In addition, since our model is built on blocks, or more specifically macroblocks, Region of Interest (ROI) coding can also be achieved. More bits can be assigned to the ROI by using a lower quantization parameter (QP) so that the perceptual quality can be maintained within the limited bit-rate. Our macorblock-level R-Q model, compared with the traditional frame-level RQ model, is more flexible and can achieve the target bit rate more accurately.

關鍵字(中)

★ 位元率-量化模型
★ 位元率控制
★ 興趣區域
★ H.264/AVC

關鍵字(英)

★ H.264/AVC
★ ROI
★ Rate Control
★ Rate-Quantization Model

論文目次

第一章緒論..........................................1
1.1 研究動機與目的.................................1
1.2 研究貢獻.......................................2
1.3 論文架構.......................................3
第二章 H.264編碼標準與位元率控制.....................5
2.1 H.264編碼標準..................................5
2.2 H.264位元率控制................................8
2.3 相關研究與探討................................11
2.3.1 R-Q Model.....................................11
2.3.2 H.264之ROI應用................................14
第三章 Rate-Quantization Model......................16
3.1 MB內容複雜度..................................16
3.1.1 一般MB內容複雜度............................16
3.1.2 模擬畫面內預測之MB複雜度....................16
3.2 MB位元數與複雜度之關係........................18
3.3 模型建立......................................20
3.4 R-Q model之精進...............................26
第四章 ROI視訊編碼之應用............................28
4.1 ROI MAP.......................................28
4.1.1 Itti saliency map演算法.....................28
4.1.2 ROI分層.....................................30
4.2 位元率分配之緩衝區控制........................33
4.3 ROI Layer QP設定............................34
4.4 Scene change的發生..........................36
第五章實驗結果....................................38
5.1 使用R-Q Model之效能分析....................38
5.1.1 原始R-Q Model位元率精準度比較.................39
5.1.2 精進之R-Q Model位元率精準度比較...............39
5.1.3 結合R-Q Model與ROI位元率精準度比較............40
5.2 PSNR之比較.................................44
5.3 Rate Control之效能分析.....................46
5.4 人眼視覺之比較.............................48
第六章結論與未來工作..............................50
參考文獻............................................51

參考文獻

[1] W. K. Pratt, Digital Image Processing. New York:Wiley, 1978, ch. 10.
[2] A. N. Netravali and J. O. Limb, “Picture coding: A review,” Proc. IEEE, vol. PROC-68, no. 3, pp. 7–12, Mar. 1960.
[3] R. C. Reininger and J. D. Gibson, “Distributions of the two-dimensional DCT coefficients for images,” IEEE Trans. Commun., vol. COM-31, no. 6, pp. 835–839, Jun. 1983.
[4] S. R. Smooth and R. A. Lowe, “Study of DCT coefficients distributions,” in Proc. SPIE, Jan. 1996, pp. 403–311.
[5] F. Muller, “Distribution shape of two-dimensional DCT coefficients natural images,” Electron. Lett., vol. 29, no. 22, pp. 1935–1936, Oct. 1993.
[6] T. Eude, R. Grisel, H. Cherifi, and R. Debrie, “On the distribution of the DCT coefficients,” in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, vol. 5, Apr. 1994, pp. 365–368.
[7] N. Kamaci, Y. Altunbasak, and R. M. Mersereau, “Frame bit allocation for the H.264/AVC video coder via a cauchy-density-based rate and distortion models,” IEEE Trans. Circuits Syst. Video Technol., vol. 15, no. 8, pp. 994–1006, Aug. 2005.
[8] X. Jing, L.-P. Chau, and W.-C. Siu, “Frame complexity-based rate-quantization model for H.264/AVC intraframe rate control,” IEEE Signal Process. Lett., vol. 15, no. 1, pp. 373–376, 2008.
[9] W.-J. Tsai and T.-L. Chou, “Scene Change Aware Intra-Frame Rate Control for H.264/AVC,” IEEE Trans. Circuits Syst. Video Technol., vol. 20, no. 12, pp. 1882–1886, Dec. 2010.
[10] C.-W. Tang, “Spatiotemporal Visual Considerations for Video Coding,” IEEE Trans. Mutimedia, vol. 9, no. 2, pp. 231–238, Feb. 2007.
[11] Y. Liu, Z.-G. Li, 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, pp. 134–139, Jan. 2008.
[12] L. Itti, C. Koch, and E. Niebur, “A Model of Saliency-Based Visual Attention for Rapid Scene Analysis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 11, pp. 1254–1259, Nov. 1998.
[13] J. Harel, C. Koch, and P. Perona, “Graph-based visual saliency”. Advances in Neural Information Processing Systems, 19:545–552, 2007.
[14] S. Lee, M. S. Pattichis, and A. C. Bovik, “Foveated video quality assessment,” IEEE Trans. Multimedia, vol. 4, no. 1, pp. 129–132, Mar.2002.
[15] JM 17.2 Reference Software. [Online]. Available: http://iphome.hhi.de/suehring/tml/download/.

指導教授

蘇柏齊(Po-chyi Su)

審核日期

2011-8-18

推文