光場技術透過全面的場景描述提升了圖像處理能力，使得應用如重新對焦、
擴增實境和 3D 顯示成為可能。然而，用於成像和顯示的專門硬體需要進行演
算法優化。伴隨而來的高資料存取和計算需求帶來了重大挑戰。本論文提出藉
由演算法與硬體協同設計達到增強應用能力，並提升運算與記憶體存取效率，
解決光場成像和顯示中的計算挑戰。

在光場成像中，深度圖對於連接各個視角的像素至關重要。使用光場立體
匹配估計深度面臨著 DRAM 頻寬和功耗的重大挑戰。在我們的目標 Full HD
場景中，可能需要高達 6.4 GB/s 的頻寬和 153 nJ/label 的能耗。我們引入了
一種光場立體匹配的演算法-硬體協同設計，以提升計算與記憶體存取的效率，
將 DRAM 頻寬減少了 53%，硬體複雜度降低了 81.5%，而相對深度誤差僅為
4.61%。在 40nm 佈局模擬和 FPGA 實現中，可以在 30 fps 下實現 4K-UHD 和
Full-HD 深度估計，分別消耗 24.5 pJ/label 和 95 pJ/label。

光場技術還使 3D 顯示成為可能，其中分解式光場顯示器提供了高解析度、
多視角的裸視立體 3D 體驗。然而，分解式光場顯示器中時間調制可能導致在
播放期間的瑕疵與視覺閃爍。我們提出了時間融合演算法，將閃爍減少了 91%，
並將播放品質提高了 5.0 dB。此外影片分解也面臨著低記憶體存取效率的問題，
成為計算瓶頸，使得每個子幀運行時間長達 2.6 秒。為解決這個問題，我們提
出了一種基於 GPU 加速的立方體視頻分解演算法，提高了記憶體存取效率，並
為時間融合演算法提供了 4-5 倍的速度提升，而不影響品質。

本論文展示了硬體與演算法協同設計如何有效地滿足先進光場成像和顯示技
術的需求。通過解決應用挑戰並提升運算和記憶體存取效率以加速處理，本研
究為未來光場成像和顯示技術的進步奠定了基礎。

Light fields enhance image processing with comprehensive scene descriptions, enabling applications such as refocusing, augmented reality, and 3D displays. However, specialized hardware for imaging and displaying requires algorithms for optimization. The accompanied computational and memory demands pose significant challenges. This dissertation introduces algorithm and hardware co-designs
to enhance application capabilities, and to improve computational and memory efficiency for light field imaging and display.

In light field imaging, depth maps are crucial for linking pixels across views. Estimating depth using light field stereo matching face major challenges on DRAM bandwidth and power consumption. It could demand up to 6.4 GB/s and 153 nJ/label for our target Full HD scenarios. We introduce an algorithm-hardware co-design for light-field stereo matching to enhance both computational and memory efficiency, reducing DRAM bandwidth by 53% and hardware complexity by 81.5%, with only 4.61% relative depth error. Implementations with 40nm lay out simulation and FPGA emulation can achieve 4K-UHD and Full-HD depth estimation at 30 fps, consuming 24.5 pJ/label and 95 pJ/label respectively.

Light fields enable high-resolution, multi-view autostereoscopic 3D factored displays. However, the inherent temporal modulation can cause visual artifacts and flickering during video playback. We propose the Temporal Fusion factorization to reduce flickering by 91% and enhance video quality by 5.0 dB. On the other hand, video factorization faces low memory efficiency, creating a 2.6-second runtime bottleneck per sub-frame. To address this, a hardware-oriented factorization for Temporal Fusion–Cuboid-wise Factorization–is proposed. It improves memory efficiency on GPU and provides 4-5x speed-ups for Temporal Fusion without compromising quality.

Overall, this dissertation demonstrates how algorithm-hardware co-design can enhance the application capabilities and increase computational and memory efficiency for light field imaging and display technologies. By addressing the inherent challenges and providing high-efficiency light field processing, this work lays a foundation for future advancements in light field imaging and display.

摘要 i
Abstract iii
誌謝 v
1 Overview of Dissertation 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3.1 Light-Field Depth Estimation . . . . . . . . . . . . . . . . . 8
1.3.2 Light-Field Factored Display . . . . . . . . . . . . . . . . . 8
1.4 Dissertation Organization . . . . . . . . . . . . . . . . . . . . . . . 9
2 Light-Field Depth Estimation 11
2.1 Light-Field Depth Estimation . . . . . . . . . . . . . . . . . . . . . 12
2.1.1 Algorithm Framework . . . . . . . . . . . . . . . . . . . . . 13
2.1.2 Design Challenges . . . . . . . . . . . . . . . . . . . . . . . 17
2.1.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2 Satellite Light Field and Image-Guided Inference . . . . . . . . . . 20
2.2.1 Satellite Light Field . . . . . . . . . . . . . . . . . . . . . . 21
2.2.2 Image-Guided Inference and Upsampling . . . . . . . . . . . 22
2.2.3 Proposed Hardware Design . . . . . . . . . . . . . . . . . . 25
2.2.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . 27
2.3 Long-Range Octave Depth Label Sampling . . . . . . . . . . . . . . 31
2.3.1 Octave Depth Label Sampling . . . . . . . . . . . . . . . . . 31
viii
2.3.2 Pyramid Memory Architecture . . . . . . . . . . . . . . . . 33
2.3.3 Proposed Hardware Design . . . . . . . . . . . . . . . . . . 36
2.3.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . 37
2.4 Hardware Implementation and Demonstration System . . . . . . . . 40
2.4.1 VLSI Implementation . . . . . . . . . . . . . . . . . . . . . 42
2.4.2 FPGA Implementation . . . . . . . . . . . . . . . . . . . . . 43
2.4.3 View-Scalable Operations . . . . . . . . . . . . . . . . . . . 43
2.4.4 Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.4.5 Real-time 5-view Light-Field Stereo Matching Demonstration 46
2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3 Light-Field Factored Display 51
3.1 Factored Display and Light Field Factorization . . . . . . . . . . . 51
3.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.1.2 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.1.3 Proposed Methods Overview . . . . . . . . . . . . . . . . . 53
3.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.2.1 Notations for 4D Light Field and Factored Display . . . . . 54
3.2.2 Frame-based Factorization for Stop-Motion Objective . . . . 56
3.2.3 Frame-based Backward Residue Removal . . . . . . . . . . . 58
3.2.4 Sample-and-Hold Formulation . . . . . . . . . . . . . . . . . 60
3.3 Temporal Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.3.1 Continuous-Time Objective and optimization . . . . . . . . 61
3.3.2 Causal Continuous-Time Objective and optimization . . . . 63
3.3.3 Derivation of atf−c[n] and btf−c[n] for TF-C . . . . . . . . . 64
3.3.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . 64
3.3.5 Visual Quality . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.4 Cuboid-wise Factorization . . . . . . . . . . . . . . . . . . . . . . . 70
3.4.1 Spatial-Temporal Cuboid-Wise Factorization . . . . . . . . . 70
3.4.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . 73
3.5 High-Refresh-Rate Display Prototype . . . . . . . . . . . . . . . . . 75
3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
ix
4 Discussion 77
4.1 Necessity of multi-rank factorization . . . . . . . . . . . . . . . . . 77
4.2 Block-Wise Factorization for Static Light Field . . . . . . . . . . . 79
4.2.1 Convergence Verification for Cuboid/Block-wise Factoriza-
tion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.3 Further Acceleration for Light-field Factorization. . . . . . . . . . . 81
4.3.1 Light-field pre-filtering. . . . . . . . . . . . . . . . . . . . . 81
4.3.2 Color Sequential Initialization. . . . . . . . . . . . . . . . . 81
5 Related Works 83
5.1 Light-Field Depth Estimation . . . . . . . . . . . . . . . . . . . . . 83
5.1.1 Depth Estimation . . . . . . . . . . . . . . . . . . . . . . . 83
5.1.2 Stereo Matching . . . . . . . . . . . . . . . . . . . . . . . . 86
5.2 Light-Field Factored Display . . . . . . . . . . . . . . . . . . . . . . 91
5.2.1 3D Display . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
5.2.2 Light Field Displays . . . . . . . . . . . . . . . . . . . . . . 94
5.2.3 Light Field Factorization Algirthms . . . . . . . . . . . . . . 97
6 Conclusion 99
6.1 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
6.1.1 View Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . 99
6.1.2 Vergence-Accommodation Conflict . . . . . . . . . . . . . . 100
6.1.3 Augmented Factorization . . . . . . . . . . . . . . . . . . . 100
6.1.4 Quality Assessment . . . . . . . . . . . . . . . . . . . . . . . 101
6.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

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