簡韶逸臺灣大學：電機工程學研究所鄧智生Tang, Chi-SunChi-SunTang2007-11-262018-07-062007-11-262018-07-062007http://ntur.lib.ntu.edu.tw//handle/246246/53205現實生活中之可見亮度由 0cd/m2 至數千億 cd/m2 不等。以往在 影像採集設備上因為頻寬、儲存容量以及一般顯示器之顯示能力不足等因數考量下，只能以 8bit 對亮度作量化。其中在訊號轉換與量化失真所損失之資訊，在要求高傳真、高清晰度顯示設備之今天，為影像再往上提升之主要障礙。而在生醫影像、太空探勘等需要高傳真動態影像資訊之任務中，能夠進行高動態範圍影像擷取之設備，達到更少失真為該領域發展之重要因數。 可是高動態範圍影像擷取之技術，有著資料量極大，傳統方法沒辦 法相容處理和硬體設計困難等問題。而在消費者的角度，能夠跟以往的設備相容以及成本低為最大的考量。在今天顯示器快速發展的環境下(LCD/OLED/Plasma...)，一般的顯示器已經可以表達高達100,000:1 之動態範圍 (相較於3、4 年前主流的300:1)。可是在高動態範圍影像擷取、儲存以及壓縮的演算法上，才剛剛起步。台灣如果需要在顯示器的市場開發與國外進行高畫質之競爭，高動態範圍影像為一可以早期切入之方向。 為了解決上述高動態範圍影像擷取、儲存以及壓縮的演算法和硬體 設計困難等問題，我提出了從演算法的創新、硬體設計問題上的克服方法等；而同時考慮到與以往設備相容之實作。演算法的特點是考慮到高動態範圍影像與一般動態範圍影像之相關性(correlation)，利用統計上的分析法以及利用人類視力的特點，能夠達到最大的壓縮和可接受之失真，並以IC 設計來對以上系統作實作。Real world luminance ranges from a few cd/m2 at dark night to over thousands of billions cd/m2 under direct sunlight. For typical sceneries, the dynamic range which is defined as the maximum to minimum luminance ratio ranges from 3 to 10 orders of magnitudes. High Dynamic Range Imaging and Video can bring about much more realistic image to the audience. In other fields such as medical imaging (CT, MRI) or space exploration imaging, any details in the original scene have too high a price to discard and High Dynamic Range Video will be a suitable tool that can assist the development in these fields. In scene-referred capturing, we try to capture the whole range of luminance in the real scene with every possible effort to preserve visual details. Preserving every detail causes a lot of bandwidth, storage spaces; and most importantly, we will need a new video/image encoding and compressing method and system. What is lacking on the path to bring High Dynamic Range video to users is High Dynamic Range encoding systems.In this research, we have conducted analysis on the problem about the encoding and compression difficulties in High Dynamic Range Video. We have proposed a backward compatible High Dynamic Range video encoding algorithm and the respective hardware implementation. The proposed algorithm takes into account the high correlation between the normal and High Dynamic Range Image.Apart from the goal of achieving high compression ratio, other aspects such as proportionality of dynamic range versus bit-rate, flexibility for users to use different TMO, dynamic range scalability and dynamic range adjustments capabilities are considered and implemented in our algorithm. In conclusion, we have proposed a novel algorithm in HDR video encoding which fulfills several scalability and flexibility requirements. In the algorithm we have proposed a new HDR-LDR prediction method, a LALEMO method and a flexible automatic multi-level prediction model. Hardware implementation proves that the algorithm is realizable. We believe that with all the above virtues and benefits, the algorithm and methods proposed could become one of the important factors in bringing HDR video to real life enjoyments.Abstracts I Table of Contents V List of Figures IX List of Tables XIV Chapter 1 Introduction 1 1.1 High Dynamic Range Imaging as an Emerging Trend 2 1.2 Realistic Imaging 3 1.3 HDR Applications 3 1.4 Thesis Organization 5 Chapter 2 High Dynamic Range Imaging 6 2.1 HDR Acquisition and Display 7 2.2 Tone Mapping Operators (TMO) 10 2.2.1 TMO and Display Device 10 2.2.2 TMO Algorithms 10 2.2.3 TMO and HDR-LDR correlation 15 2.3 High Dynamic Range Video Encoding 17 2.3.1 The Problem 18 2.3.2 Prior Art 19 2.3.3 The Proposed Solution 20 Chapter 3 Proposed Algorithm for 22 High Dynamic Range Video Encoding 3.1 Algorithm overview 23 3.1.1 Backward compatible HDR encoding 24 3.1.2 Flexible choice of TMO 26 3.1.3 Dynamic Range Scalability 27 3.1.4 Dynamic Range Adjustment 29 3.2 HDR data Preprocessing 30 3.2.1 Luminance Encoding 31 3.2.2 Local Adaptation Luminance Estimation 34 3.3 HDR-LDR prediction 41 3.3.1 Luminance grouping and model map 42 3.3.2 Prediction and model curve 46 3.3.3 Residue blocks 49 3.4 Prediction models encoding 51 3.4.1 Model map encoding 52 3.4.2 Model curve encoding 54 3.4.3 Residue blocks processing 56 3.5 Dynamic Range modulation 58 3.6 Visual Quality assessment and Compression Performance 59 Chapter 4 Implementation 65 4.1 System overview 66 4.1.1 Specification 68 4.1.2 Implementation 70 4.2 System Platform and Processing Units 75 4.2.1 Adaptation Kernel Engine (AKE) 79 4.2.2 Adaptive Blocking Engine (ABE) 82 4.2.3 Feature Vector Engine (FVE) 84 4.2.4 Luminance Grouping Engine (LGE) 86 4.2.5 Model Coding Engine (MCE) 89 4.3 Simulations and testing environment 91 4.4 Implementation results 94 Chapter 5 Conclusion 95 Bibliography 993040721 bytesapplication/pdfen-US高動態範圍視訊壓縮影像擷取High Dynamic RangeHDRHDRITMOTone-mappingVideo CompressionVideo EncodingVideo Encoderthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/53205/1/ntu-96-J93921054-1.pdf