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Analysis of Affine Motion Compensated Prediction and its Application in Aerial Video Coding / Titelei/Inhaltsverzeichnis
Analysis of Affine Motion Compensated Prediction and its Application in Aerial Video Coding / Titelei/Inhaltsverzeichnis
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I–XVIII
Titelei/Inhaltsverzeichnis
I–XVIII
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1–10
1 Introduction
1–10
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1.1 Motion-Compensated Prediction
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1.2 Challenges for Aerial Surveillance Video Coding
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1.2.1 Region of interest-based video coding
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1.3 Contributions
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1.4 Outline
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11–36
2 Basics
11–36
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2.1 SceneModel
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2.2 CameraModel
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2.2.1 Perspective projection
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2.2.2 Lens model
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2.2.3 Sensor model
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2.2.4 Homogeneous coordinates
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2.2.5 World coordinates to camera coordinates
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2.3 Projective Transformation and Homography
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2.4 Motion Estimation from Image Sequences
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2.4.1 Feature detection
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2.4.2 Correspondence analysis by Kanade-Lucas-Tomasi feature tracking
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2.4.3 Outlier removal: random sample consensus (RANSAC)
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2.5 Mosaicking of Aerial Videos
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2.6 Hybrid Video Coding
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2.6.1 Motion-compensated prediction
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2.6.2 Global motion compensation
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2.7 Rate-Distortion ¿eory
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2.8 Region of Interest- (ROI-) based Video Coding
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2.8.1 ROI definition and detection
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2.8.2 ROI encoding
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37–64
3 Rate-Distortion Theory for Affine Motion Compensation in Video Coding
37–64
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3.1 Efficiency Analysis of Fully AffineMotion Compensation
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3.1.1 Affine motion and error model
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3.1.2 Probability density function of the displacement estimation error
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3.1.3 Power spectral density of the signal
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3.1.4 Power spectral density of the displacement estimation error
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3.1.5 Rate-distortion function
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3.1.6 Rate-distortion analysis of affine global motion-compensated prediction
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3.1.7 Conclusions for the fully affine motion model for global motion compensation
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3.2 Efficiency Analysis of Simplified AffineMotion Compensation
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3.2.1 Derivation of the probability density function of the displacement estimation error for a simplified affine model
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3.2.2 Rate-distortion analysis of the simplified affine model
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3.3 Summary of AffineMotion-Compensated Prediction in Video Coding
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65–86
4 ROI-based System for Low Bit Rate Coding of Aerial Videos
65–86
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4.1 ROI: New Areas (NAs)
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4.1.1 Calculation of the new areas
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4.1.2 Long-term mosaicking of aerial videos
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4.1.3 In-loop radial distortion compensation
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4.2 ROI:Moving Objects (MOs)
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4.2.1 Highly performant difference image-based moving object detection
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4.3 ROI Coding of Aerial Video Sequences
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4.3.1 Inherent noise removal of the proposed general ROI coding
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4.4 Mosaicking of ROI-Encoded Videos
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4.5 Video Reconstruction from ROI-Encoded Videos
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87–116
5 Experiments
87–116
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5.1 AffineMotion Compensation in Video Coding
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5.1.1 Efficiency measurements for fully affine motion-compensated prediction in video coding
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5.1.2 Operational rate-distortion diagrams using JEM
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5.2 Evaluation of the ROI-based System for Low Bit Rate Aerial Video Coding
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5.2.1 Objective evaluation of the general ROI-coding system compared to a modified HEVC-encoder and common HEVC coding
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5.2.2 Subjective tests
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5.2.3 Long-term mosaicking
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117–132
6 Summary and Conclusions
117–132
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A Appendix
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A.1 Displacement Estimation Error pdf Derivation (Fully AffineModel)
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A.2 Displacement Estimation Error pdf Derivation (Simplified AffineModel)
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A.3 Fourier Transform of Displacement Estimation Error (Fully AffineModel)
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A.4 Fourier Transform of Displacement Estimation Error (Simplified AffineModel)
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133–152
Bibliography
133–152
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Analysis of Affine Motion Compensated Prediction and its Application in Aerial Video Coding , page I - XVIII
Titelei/Inhaltsverzeichnis
Autoren
Holger Meuel
DOI
doi.org/10.51202/9783186865106-I
ISBN print: 978-3-18-386510-9
ISBN online: 978-3-18-686510-6
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