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Arch Design of Video Processing System on Chip
xvii
1 Introduction and motivation 1
1.1 Problem statement . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Trends and developments of TV systems . . . . . . . . . . . 4
1.3 Functional requirements . . . . . . . . . . . . . . . . . . . . 9
1.4 Computational e®ort . . . . . . . . . . . . . . . . . . . . . . 11
1.5 Architectural requirements . . . . . . . . . . . . . . . . . . . 12
1.6 Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . 15
1.7 Background and motivation of the chapters . . . . . . . . . 18
1.8 The main contributions of the author . . . . . . . . . . . . . 21
2 Developments in video computing architectures 23
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2 Exploiting parallelism in computing systems . . . . . . . . . 24
2.3 Aspects of application-speci¯c parallelism . . . . . . . . . . 28
2.4 Parallelism and control . . . . . . . . . . . . . . . . . . . . . 31
2.5 Examples of media processor architectures . . . . . . . . . . 33
2.5.1 Introduction to media processors . . . . . . . . . . . 33
2.5.2 The Video Signal Processor . . . . . . . . . . . . . . 37
2.5.3 The Multimedia Video Processor (MVP) . . . . . . 40
2.5.4 The TriMedia processor . . . . . . . . . . . . . . . . 44
2.5.5 The Emotion Engine . . . . . . . . . . . . . . . . . . 51
2.6 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . 57
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x Contents
3 Examples of video functions and their expenses 61
3.1 Tradeo®s in system design . . . . . . . . . . . . . . . . . . . 61
3.2 Sharpness enhancement . . . . . . . . . . . . . . . . . . . . 63
3.2.1 Introduction to sharpness enhancement . . . . . . . 63
3.2.2 Local intensity level and related noise visibility . . . 65
3.2.3 Local sharpness of the input signal . . . . . . . . . . 67
3.2.4 Noise contained by the signal (adaptive coring) . . . 67
3.2.5 Prevention of aliasing from non-linear processing . . 70
3.2.6 The system including all controls . . . . . . . . . . . 73
3.2.7 Results and conclusions . . . . . . . . . . . . . . . . 74
3.3 Advanced sampling-rate conversion . . . . . . . . . . . . . . 76
3.3.1 Introduction to video scaling . . . . . . . . . . . . . 76
3.3.2 Basic theory of sampling-rate conversion . . . . . . . 77
3.3.3 Considerations for SRC implementation . . . . . . . 79
3.3.4 Transposition of a sampling-rate converter . . . . . . 81
3.3.5 Requirements for transposed ¯lters . . . . . . . . . . 83
3.3.6 Experiments and results for polyphase ¯lters . . . . 84
3.3.7 Conclusions on video scaling . . . . . . . . . . . . . 86
3.4 Computational costs of video functions . . . . . . . . . . . . 86
3.4.1 Estimation model for complexity . . . . . . . . . . . 86
3.4.2 Sharpness Enhancement complexity estimation . . . 88
3.4.3 Sampling-rate conversion complexity estimation . . . 92
3.4.4 Temporal noise reduction . . . . . . . . . . . . . . . 93
3.4.5 Motion-Compensated frame-rate conversion . . . . . 95
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 |
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