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研究生: 簡偉哲
Wei-Che Chien
論文名稱: 針對全彩影像提取的彩色濾波陣列資訊之新穎彩度抽樣方法
Novel Chroma Subsampling Method for RGB Full-color Image Using Extracted CFA Information
指導教授: 鍾國亮
Kuo-Liang Chung
口試委員: 陳建中
Jiann-Jone Chen
廖弘源
Hong-Yuan Liao
范國清
Kuo-Chin Fan
李同益
Tong-Yee Lee
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: 2018
畢業學年度: 106
語文別: 英文
論文頁數: 39
中文關鍵詞: Bayer彩色濾波陣列影像區塊失真最小化彩色濾波陣列資訊提取彩度抽樣解馬賽克高效率視頻編碼品質品質位元率權衡搜尋演算法
外文關鍵詞: Bayer color filter array image, Block-distortion minimization, CFA information extraction, Chroma subsampling, Demosaicking, High Efficiency Video Coding, Quality, Quality-bitrate tradeoff, Search algorithm
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在這篇論文,我們提出一個新穎彩度抽樣模型針對RGB全彩影像使用提取的彩色濾波陣
列資訊。彩色濾波陣列資訊可以被提取,首先藉由相機模組辨識方法辨識出全彩影像的相機
模組,然後將辨識的相機模組當作查詢金鑰從對照表去提取出彩色濾波陣列結構和模組資
訊。接著,使用提取的彩色濾波陣列資訊,我們提出一個彩色濾波陣列區塊失真最小化彩度
抽樣架構,叫作CFA-BMCS。更進一步,針對CFA-BMCS,我們提出一個幾何搜尋演算法,
針對2x2 UV 區塊去決定最好的彩度抽樣(U, V)值。在知名的Dresden影像測試集,根據83
張RGB全彩影像,實驗結果展示在目前的高效率視頻編碼軟體,版本HM-16.17,在還原的
影像上,和六個存在的彩度抽樣方法比較,提出的CFA-BMCS方法有可觀的品質和品質位
元率權衡優勢。除此之外,我們修改提出的CFA-BMCS方法來解決輸入影像為Bayer彩度濾
波陣列影像的情況。實驗結果展示修改的CFA-BMCS方法在還原的Bayer彩度濾波陣列影像
上,優於2個最新的方法。


In this thesis, we propose a new chroma subsampling model for the RGB full-color image I^RGB using the extracted color filter array (CFA) information of I^RGB. The CFA information can be extracted by first applying the camera model identification method to recognize the camera model used to capture I^RGB, and then taking the recognized camera model as the query key to extract the CFA structure and module information from the lookup table built up in advance. Next, using the extracted CFA information, we propose a CFA block-distortion minimization-based chroma subsampling scheme, called CFA-BMCS. Further, we propose a geometry-based search algorithm to determine the best subsampled chroma (U, V)-pair for each 2x2 UV block for CFA-BMCS. Based on the 83 RGB full-color images in the well-known Dresden set, the experimental results demonstrated that in the current High Efficiency Video Coding (HEVC) reference software HM-16.17, the proposed CFA-BMCS method has substantial quality and quality-bitrate tradeoff merits of the reconstructed images when compared with the six existing chroma subsampling methods. In addition, we modify the proposed CFA-BMCS method to solve the chroma subsampling problem when the input image is the Bayer CFA image. The experimental results showed that the quality of the reconstructed Bayer CFA image by the modified CFA-BMCS method is clearly superior to the two state-of-the-art methods.

指導教授推薦書 i 論文口試委員審定書 ii 中文摘要 iii Abstract in English iv 誌謝 v Contents vi List of Figures viii List of Tables ix 1 Introduction 1 1.1 The Weaknesses of Existing Traditional Chroma Subsampling Methods and Motivation 1 1.2 Contribution 3 2 The Proposed New Chroma Subsampling Scheme for I^RGB: CFA-BMCS 5 2.1 The Proposed CFA-based Chroma Subsampling Model 5 2.2 The Proposed CFA-BMCS Scheme 6 3 The Proposed Geometry-based Search Algorithm to Realize CFA-BMCS and Application 9 3.1 The Proposed Geometry-based Search Algorithm 9 3.1.1 Determining the Search Area for Each 2x2 Chroma Block 10 3.1.2 Example Simulation 12 3.1.3 Computational Complexity Analysis 12 3.1.4 Accuracy Analysis 13 3.2 Application to Solve the Chroma Subsampling Problem When the Input is Bayer CFA Image 14 3.2.1 The Proposed B-BMCS Chroma Subsampling Method 15 3.2.2 The Difference between B-BMCS and Two State-of-the-art Methods 16 3.2.3 Quality Merit of B-BMCS over Chen et al.’s and Lin et al.’s Methods 17 4 Experimental Results 21 4.1 PSNR, CPSNR, and SSIM Quality Merits 21 4.1.1 PSNR Merit 21 4.1.2 CPSNR and SSIM Merits 22 4.2 Quality-bitrate Tradeoff Merit 24 5 Conclusion 25 6 APPENDIX 26

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