臺灣博碩士論文加值系統

隨著科技的日新月異，光場相機已逐漸取代傳統相機，而各種光場應用中，深度資訊扮演了極為重要的角色。相較於傳統平面影像，還原擁有視差的影像能提供較高品質的深度資訊，立體場景較能完整呈現，可帶給人們更深的立體感受。即使近年來演算法的迅速發展，目前高水準的視差算演算法仍然無法符合即時影像生成的需求。產生即時且準確的視差深度圖已成為現今研究中的一項挑戰。

視差計算可被定義為在二維度馬可夫隨機場的一種能量最小化問題。在多種整體最佳化的方法中，信賴傳遞演算法提供了高品質的結果並且有很多的好處於硬體實作上，有著很高的潛力達到即時影像處理的需求。信賴傳遞演算法主要是考量每一節點與鄰近節點的相互關係，配合節點本身可能的機率分布，決定最佳的深度資訊。再利用節點之間的訊息傳遞，達成最後收斂的效果，找出一組最佳的深度資訊，進行深度修補步驟。然而，由於迭代的程序，信賴傳遞演算法需要很高的頻寬、記憶體以及運算成本。方格信賴傳遞演算法大幅節省記憶體的使用量，並能保有訊息交換的資訊。

本論文首先探討多元光場組態下使用多基準線視差估計演算法計算所得之中央視差深度資訊，針對不同視圖數量以及視圖組態分析資料計算時間及視差深度表現。接著討論現行信賴傳遞演算法在硬體實作上遭遇的問題，由於圖片解析度的提高，深度精細度也隨之必須提高，造成了原始訊息更新方式在硬體實作上造成面積極大的困擾，而軟體上高效率的演算法因為訊息之間的相依性，使之於硬體實作時無法達到即時更新的需求。先期曾經提出之快速訊息更新方式，雖然能夠提供高解析度之視差計算結果，但因做了簡單的假設使得品質有部分犧牲，在視圖數量較少的狀況下品質落差更為嚴重。

本論文並針對複雜度的簡化提出了硬體上有效率的訊息計算方式，能在高精細度且不犧牲任何品質的狀況下，大量降低運算複雜度，保有效能，並能在硬體架構上實行。最後，在方格信賴傳遞演算法為基礎的架構下，提出了新的訊息更新處理單元，節省了極大部分信賴傳遞演算法硬體架構面積。

With the rapid improvement of technology, light-field cameras have been growing more and more popular. Disparity map plays an important role for many light-field applications in light-field systems. Despite recent advances, state-of-the-art algorithms fail to generate a precise disparity map rapidly enough for VLSI real time processing. To generate real-time and accurate disparity map has become the bottleneck for current research.

Disparity estimation can be formulated as an energy minimization problem on a 2D Markov Random Fields (MRFs). Among many MRF global optimization method, belief propagation (BP) is a popular global optimization algorithm which gives high quality and has several advantages for hardware implementation and highly potential to achieve real-time processing. However, it requires high bandwidth, memory and computational costs because of costly iterative operations, the original belief propagation is computationally expensive for real-time system implementation. Tile-based BP is an efficient improved BP for hardware implementation. Boundary messages loaded from different directions have been performed with different iterations to reduce high memory and bandwidth requirements.

In this thesis, we first examine variant view configurations of light fields for multi-baseline disparity estimation for central view. We analyze the computation time of data cost generation against different number of views and the performance of each kind of variant light-field configurations for multi-baseline setting. After analysis on variant light-field configurations, we introduce current techniques of belief propagation and examine the problems of existing methods. Followed by introducing a novel hardware-efficient message-update method to remove redundant computational costs during message-update based on tile-based BP. We focus on the algorithm and hardware architecture design of multi-baseline disparity estimation from light fields. At first, we analyze the hardware cost in the belief propagation system, and indicate the challenge and bottleneck in the area and latency resource requirement to preserve high-quality in light fields. We analyze the computation time in the view of software implementation. After software analysis, we analyze the latency and area of the belief propagation from the perspective of hardware implementation. And then we exploit the unique characteristics of the generalized truncated linear model of the smoothness term in the Markov random field and propose an efficient hardware-oriented message-update algorithm from the aspect of hardware implementation with corresponding message-update process element (PE). This message-update PE indeed reduce the complexity of message construction for belief propagation, and hence greatly reduce the area and cycle counts.

The Authorization of Oral Members for Research Dissertation i
Acknowledgement iii
Abstract in Chinese v
Abstract ix
1 Introduction 1
1.1 Light Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Light-field Cameras . . . . . . . . . . . . . . . . . . . . . . . 7
1.3 Stereo Vision . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.4 Depth and Disparity . . . . . . . . . . . . . . . . . . . . . . 12
1.5 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . 16
2 Study of Disparity Estimation for Central View 17
2.1 Background of Disparity Estimation and Belief Propagation 18
2.1.1 Intro duction to Markov Random Fields . . . . . . . . 20
2.1.2 Similarity Measurement . . . . . . . . . . . . . . . . 26
2.1.3 Finding Optimal Lab els using Tile-based BP . . . . . 28
2.2 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3 Analysis of Variant Light-field Configurations for Multibaseline Disparity Estimation for Central View 35
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2 Proposed System . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2.1 Multi-baseline Disparity Estimation for Central View 41
3.2.2 Variant light-field configurations . . . . . . . . . . . . 42
3.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . 43
3.4 Comparison and Discussion . . . . . . . . . . . . . . . . . . 55
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4 Problem with Multi-baseline Disparity Estimation using Belief Propagation 65
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.2 Original Message-update Algorithm . . . . . . . . . . . . . . 70
4.3 Efficient Message-update Algorithms . . . . . . . . . . . . . 71
4.3.1 Two-pass Message-update Algorithm . . . . . . . . . 75
4.3.2 Fast Message-update Algorithm . . . . . . . . . . . . 77
4.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . 79
4.5 Comparison and Discussion . . . . . . . . . . . . . . . . . . 82
4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
5 Architecture Design for Disparity Estimation for Central
View in Light Fields 87
5.1 System Overview . . . . . . . . . . . . . . . . . . . . . . . . 88
5.2 Software Analysis . . . . . . . . . . . . . . . . . . . . . . . . 90
5.3 Proposed Message-update Algorithm . . . . . . . . . . . . . 95
5.4 Processing Element Design . . . . . . . . . . . . . . . . . . . 97
5.4.1 Hardware Analysis . . . . . . . . . . . . . . . . . . . 100
5.5 Comparison and Discussion . . . . . . . . . . . . . . . . . . 113
5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
6 Conclusion 117
6.1 Principal Contributions . . . . . . . . . . . . . . . . . . . . . 117
6.2 Analysis of Variant View Configurations in Light Fields . . . 118
6.3 Analysis of Disparity Estimation using Belief Propagation . 118
6.4 Efficient Message-update PE for Belief Propagation . . . . . 119
6.5 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . 120
6.5.1 Multi-baseline Disparity Estimation . . . . . . . . . . 120
6.5.2 Message-update PE for Belief Propagation . . . . . . 120
Bibliography 121

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