基於 3D 品質最佳化之 HEVC 三維視訊編碼位元率配置技術

自從 2010 年起，隨著 3D 電視、電影的興起，也越來越多人認同未來十年將是 3D 立體視訊技術快速成長的階段。然而，3D 的視訊相較於原有 2D 視訊資料量更為龐大，除了在解析度方面有所增長外，左/右眼、多視角或深度方面資訊都有傳輸需求，但網路傳輸頻寬仍然有限，因此加深了視訊編碼器的負擔，必須開發一套更有效率的編碼演算法，才能維持不錯的視訊品質。本論文係針對彩色+深度格式的 3D 視訊壓縮，基於 Scalable HEVC 編碼器 (基本層：彩色；加強層：深度) ，提出基於 3D 品質最佳化的位元率配置演算法，透過彩色/深度聯合位元率控制，使得位元率誤差較原始編碼器精準外，更可依據視訊內容中不同 3D 視訊品質重要性及彩色/深度 LCU 區塊特徵的不同而給予不同的目標位元數後，最終希望能透過位元數的有效分配而達到最佳的 3D 立體視訊品質。在本論文中，位元率分配主要有兩個部分：一是透過各聯合 LCU 區塊中因於移動向量與彩色/深度邊緣匹配程度的不同所具備不同的「3D 品質貢獻度」而分配各聯合 LCU區塊不同的位元數；二是透過 SVR 訓練模型的結果建議各聯合 LCU 區塊中彩色與深度 LCU 區塊間的位元數比例。本論文希望透過不同 LCU 區塊間的 3D 品質貢獻評估及彩色/深度 LCU 區塊間的重要性預測而進行適當的位元數分配，希望可以達到最好的 3D 立體視訊壓縮品質。透過實驗結果可發現：本系統的聯合位元率控制平均可較原始 Scalable HEVC編碼方式更精準 0.8% ，且在總共 15 種不同測試序列與不同總位元率的組合配對中，本論文演算法在其中 12 組中的 3D 立體視訊客觀品質成績有著最佳表現。

關鍵字

3D HEVC ； 3D 品質最佳化；聯合位元率控制；位元率分配；支持向量迴歸模型；聯合編碼

並列摘要

In this paper, we proposed a new algorithm in rate allocation and color+depth joint rate control based on 3D-HEVC. In our algorithm, "joint rate control" can provide a smaller bitrate error than standard SHVC coding tool. In "rate allocation," we have two method to seperate bits. First, we prposed a method to seperate different joint (color+depth) LCUs in one joint (color+depth) frame, this is called "3D Quality Contribution." In 3D quality contribution, we use motion vector and edge matching to judge each LCU whether it is important to human visual system. Another method to seperate target bits is "color/depth LCU rate allocation via SVR model." We use different features with training sequences and 9 different target bitrates to build 9 SVR model. After SVR model is established, we can use test sequences features and SVR models to get suggested best color/depth LCUs distribution rate. In our experiments, our joint rate control system can reduce bitrate error about 0.8% than standard SHM 6.0. And we have tested 15 different sequence with different target bitrate, we have the best 3D quality performance in 12 different sequence with different target bitrate.

並列關鍵字

3D HEVC ； 3D quality ； joint bitrate control ； bitrate allocation ； support vector mechine ； joint encoding

參考文獻

[2] Taichi Kawano, Kazuhisa Yamagishi, and Takanori Hayashi, “Performance Comparison of Subjective Assessment Methods for 3D Video Quality,” Proc. of Int’l Workshop on Quality of Multimedia Experience, QoMEX Workshop-2012.

[4] Bing Xiong, Xiaojiu Fan, Ce Zhu, Xuan Jing, and Qiang Peng, “Face Region Based Conversational Video Coding,” IEEE Trans. on Circuits and Systems for Video Technology, Vol. 21, No. 7, pp. 917-931, 2011.

[5] Holger Meuel, Marco Munderloh, and Jörn Ostermann, “Low Bit Rate ROI Based Video Coding for HDTV Aerial Surveillance Video Sequences,” Proc. of IEEE Conf. on Computer Vision and Pattern Recognition Workshops, CVPRW-2011.

[6] Yunlong Feng, Gene Cheung, Wai-tian Tan, and Yusheng Ji, “Hidden Markov Model for eye gaze prediction in networked video streaming,” Proc. of IEEE Int’l Conf. on Multimedia & Expo, ICME-2011, Barcelona, Spain, Jul. 2011.

[7] Chaminda T.E.R. Hewage and Maria G. Martini, “Reduced-Reference Quality Evaluation for Compressed Depth Maps Associated With Colour Plus Depth 3D Video,” Proc. of IEEE Int’l Conf. On Image Processing, ICIP-2010, Hong-kong, Sep. 2010.

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基於 3D 品質最佳化之 HEVC 三維視訊編碼位元率配置技術

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