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研究生： 黃祺超
Chi-Chao Huang
論文名稱： 針對SCI影像的自適應彩度抽樣結合亮度引導之彩度重建
Adaptive Chroma Subsampling-binding and Luma-guided Chroma Reconstruction Method for Screen Content Images
指導教授： 鍾國亮
Kuo-Liang Chung
口試委員: 蔡文祥
Wen-Hsiang Tsai
范國清
Kuo-Chin Fan
廖弘源
Hong-Yuan Liao
鍾國亮
Kuo-Liang Chung
陳建中
Jiann-Jone Chen
學位類別： 碩士
Master
系所名稱： 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
論文出版年： 2017
畢業學年度： 105
語文別： 英文
論文頁數： 31
中文關鍵詞： 彩度重建 、彩度抽樣 、全彩峰值訊噪比 、植基於梯度的結構相似度 、高效率視訊編碼 、螢幕內容影像 、贏家優先投票法
外文關鍵詞： Chroma reconstruction, Chroma subsampling, Color peak signal-to-noise ratio (CPSNR), Gradient-based structure similarity index(CGSS), High Effciency Video Coding (HEVC), Screen content images (SCIs), Winner-first voting strategy
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    • 本篇論文針對螢幕內容影像(SCI)提出了創新的自適應彩度還原方法，利用結合彩度抽樣之亮度資訊引導彩度進行重建。在得到解壓後的亮度影像與抽樣後的彩度影像後，會先使用贏家優先投票法(winner-first voting strategy)來判斷彩度的抽樣方式。接著，將解壓後的亮度影像進行與彩度影像相同的抽樣方式，以便使我們能準確的找到抽樣後的亮度影像與抽樣後的彩度影像間的關係。基於以上的方法，本篇論文還會使用自適應的動態遮罩以及亮度引導彩度的方式進行彩度還原，並會提及相關的時間複雜度分析。在影像品質量度的部分，我們使用全彩的峰值訊噪比(CPSNR)以及植基於梯度的結構相似度(CGSS)作為重建後之SCI的品質優劣標準。透過26張SCI影像，實驗結果顯示，平均起來 CS_BILINEAR-Proposed 相較於其他的抽樣與彩度重建組合有著較佳的表現，其中 `CS_BILINEAR' 表示植基於亮度資訊的彩度抽樣法，由王等人所提出，而 `Proposed' 表示本篇論文的彩度重建方法。

    In this thesis, we propose a novel adaptive chroma subsampling-binding and luma-guided chroma reconstruction method for screen content images (SCIs). After receiving the decoded luma and subsampled chroma image from the decoder, prior to compression, a fast winner-first voting strategy is proposed to identify the used chroma subsampling scheme. Then, the decoded luma image is subsampled as the identified subsampling scheme did on the chroma image such that we are able to figure out an accurate correlation between the subsampled decoded luma image and the decoded subsampled chroma image. Accordingly, an adaptive sliding window-based and luma-guided chroma reconstruction method is proposed. The related computational complexity analysis is also provided. We take the color peak signal-to-noise ratio (CPSNR) and gradient-based structure similarity index (CGSS) of the reconstructed chroma images and SCIs as the quality measurement. Based on 26 typical test SCIs, several experiments in HEVC are conducted to show that on average, the combination CS_BILINEAR-Proposed outperforms all state-of-the-art comparative combinations, where `CS_BILINEAR' denotes the luma-aware based chroma subsampling scheme by Wang et al. and `Proposed' denotes our proposed chroma reconstruction method.

    Recommendation Letter i Approval Letter ii Abstract in Chinese iii Abstract in English iv Acknowledgements v Contents vi List of Figures viii List of Tables ix 1 Introduction 1 1.1 Existing Chroma Upsampling Methods and Weaknesses 2 1.2 Motivation and Contributions 4 2 The Study of Correlation Distortion between Subsampled Luma and Chroma Block-pair 6 2.1 Correlation Distortion Affection Between the Subsampled Luma and Chroma Block-pair 6 2.2 A Metric to Measure the Correlation Distortion Degree for the Subsampled Luma and Chroma Block-pair 9 2.3 The Relation between the Correlation Distortion Degree and the Chroma Reconstruction accuracy 11 3 The Proposed Chroma Reconstruction Method 12 3.1 Luma-guided Winner-first Voting Strategy to Identify the Used Chroma Subsampling Scheme 12 3.1.1 Luma-guided Winner-first Voting Strategy 12 3.1.2 Computational Complexity Analysis 16 iv3.2 Proposed Adaptive Chroma Subsampling-binding and Luma-guided Chroma Reconstruction Method 16 3.2.1 Adaptive Luma-guided Chroma Reconstruction to Alleviate the Out of Min-Max Range Problem 17 3.2.2 Computational Complexity Analysis 18 4 Experimental Results 20 4.1 CPSNR and CGSS Quality Improvement 20 4.1.1 CPSNR and CGSS Quality Performance Comparison among the 45 Comparative Combinations in CS_1 × CR_1 20 4.1.2 CPSNR and CGSS Quality Performance Comparison among the 6 Comparative Combinations in CS_2 × CR_2 23 4.2 Visual effect merit 24 4.3 Better QP-CPSNR Trade-off 28 5 Conclusion 29 References 30

    [1] C. Chen and W. Peng, “Intra Line Copy for HEVC Screen Content Coding,” IEEE Transactions on Circuits and Systems for Video Technology, early access, 2016.
    [2] T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, Section 3.1: Asymptotic notation, Third Ed., The MIT Press, London, England, 2009.
    [3] J. Allebach and P. W. Wong, “Edge-directed Interpolation,” International Conference on Image Processing, vol. 3, pp. 707-710, Sep. 1996.
    [4] Experimental results, ftp://140.118.175.164/Experiment.
    [5] K. He, J. Sun, and X. Tang, “Guided Image Filtering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 6, pp. 1397-1409, Jun. 2013.
    [6] ISO/IEC JTC1/SC29/WG11 and ITU-T SG16/Q.6, Adaptive Basic Unit Layer Rate Control for JVT, Doc. JVT-G012. Pattaya, Thailand, 2003.
    [7] J. Korhonen, “Improved Image Fidelity by Luma-assisted Chroma Subsampling,” IEEE International Conference on Multimedia and Expo (ICME), pp. 1-6, Jul. 2015.
    [8] W. S. Kim, W. Pu, A. Khairat, M. Siekmann, J. Sole, J. Chen, M. Karczewicz, T. Nguyen, and D. Marpe, “Cross-component Prediction in HEVC,” IEEE Trans. Circuits and Systems for Video Technology, early access, 2016.
    [9] X. Li and M. T. Orchard, “New edge-directed Interpolation,” IEEE Trans. Image Process., vol. 10, no. 10, pp. 1521-1527, Oct. 2001.
    [10] Y. Lu, S. Li, and H. Shen, “Virtualized Screen: A third Element for Cloud-mobile Convergence,” IEEE Trans. Multimedia, vol. 18, no. 2, pp. 4-11, Feb. 2011.
    [11] S. Minaee and Y. Wang, “Screen Content Image Segmentation Using Least Absolute Deviation Fitting,” IEEE International Conference on Image Processing (ICIP), pp. 3295-3299, Sep. 2015.
    [12] Z. Ni, L. Ma, H. Zeng, C. Cai, and K. Ma, “Gradient Direction for Screen Content Image Quality Assessment,” IEEE Signal Processing Letters, vol. 23, no. 10, pp. 1394-1398, Oct. 2016.
    [13] Requirements for an Extension of HEVC for Coding of Screen Content, document ISO/IEC JTC1/SC29/WG11 MPEG2013/N14174, 2014.
    [14] Y. Sun, T. Chuang, J. Kim, Y. Chen, S. Liu, Y. Huang, and S. Lei, “Improved Palette Index Map Coding on HEVC SCC,” IEEE International Conference on Image Processing, pp. 4210-4214, Sep. 2016.
    [15] S. Wang, K. Gu, S. Ma, and W. Gao, “Joint Chroma Downsampling and Upsampling for Screen Content Image,” IEEE Trans. Circuits and Systems for Video Technology, vol. 26, no. 9, pp. 1595-1609, Sept. 2016.
    [16] S. Wang, K. Gu, X. Zhang, W. Lin, S. Ma, and W. Gao, “Reduced-Reference Quality Assessment of Screen Content Images,” IEEE Transactions on Circuits and Systems for Video Technology, early access, 2016.
    [17] S. Wang, L. Ma, Y. Fang, W. Lin, S. Ma, and W. Gao, “Just Noticeable Difference Estimation for Screen Content Images,” IEEE Transatcions on Image Processing, vol. 25, no. 8, pp.3838-3851, Aug. 2016.
    [18] J. Xu, R. Joshi, and R. A. Cohen, “Overview of the Emerging HEVC Screen Content Coding Extension,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 26, no. 1, pp. 50-62, Jan. 2004.
    [19] X. Xu, S. Liu, T. Chuang, and S. Lei, “Block Vector Prediction for Intra Block Copying in HEVC Screen Content Coding,” Data Compression Conference, pp. 273-282, Apr. 2015.
    [20] X. Zhang, “Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-decision Estimation,” IEEE Trans. Image Process., vol. 17, no. 6, pp. 887-896, Jun. 2008.
    [21] Y. Zhang, D. Zhao, J. Zhang, R. Xiong, and W. Gao, “Interpolation-dependent Image Downsampling,” IEEE Transactions on Image Processing, vol. 20, no. 11, pp. 3291-3296, Nov. 2011.

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