Efficient Techniques for Management and Delivery of Video Data

The rapid advances in electronic imaging, storage, data compression telecommunications, and networking technology have resulted in a vast creation and use of digital videos in many important applications such as digital libraries, distance learning, public information systems, electronic commerce, movie on demand, etc. This brings about the need for management as well as delivery of video data. Organizing and managing video data, however, is much more complex than managing conventional text data due to their semantically rich and unstructured contents. Also, the enormous size of video files requires high communication bandwidth for data delivery. In this dissertation, I present the following techniques for video data management and delivery. Decomposing video into meaningful pieces (i.e., shots) is a very fundamental step to handling the complicated contents of video data. Content-based video parsing techniques are presented and analyzed. In order to reduce the computation cost substantially, a non-sequential approach to shot boundary detection is investigated. Efficient browsing and indexing of video data are essential for video data management. Non-linear browsing and cost-effective indexing schemes for video data based on their contents are described and evaluated. In order to satisfy various user requests, delivering long videos through the limited capacity of bandwidth is challenging work. To reduce the demand on this bandwidth, a hybrid of two effective approaches, periodic broadcast and scheduled multicast, is discussed and simulated. The current techniques related to the above works are discussed thoroughly to explain their advantages and disadvantages, and to make the new improved schemes. The substantial amount of experiments and simulations as well as the concepts are provided to compare the introduced techniques with the other existing ones. The results indicate that they outperform recent techniques by a significant margin. I conclude the dissertation with a discussing of future research directions

Robust and efficient techniques for automatic video segmentation.

by Lam Cheung Fai.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 174-179).Abstract also in Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Problem Definition --- p.2Chapter 1.2 --- Motivation --- p.5Chapter 1.3 --- Problems --- p.7Chapter 1.3.1 --- Illumination Changes and Motions in Videos --- p.7Chapter 1.3.2 --- Variations in Video Scene Characteristics --- p.8Chapter 1.3.3 --- High Complexity of Algorithms --- p.10Chapter 1.3.4 --- Heterogeneous Approaches to Video Segmentation --- p.10Chapter 1.4 --- Objectives and Approaches --- p.11Chapter 1.5 --- Organization of the Thesis --- p.13Chapter 2 --- Related Work --- p.15Chapter 2.1 --- Algorithms for Uncompressed Videos --- p.16Chapter 2.1.1 --- Pixel-based Method --- p.16Chapter 2.1.2 --- Histogram-based Method --- p.17Chapter 2.1.3 --- Motion-based Algorithms --- p.18Chapter 2.1.4 --- Color-ratio Based Algorithms --- p.18Chapter 2.2 --- Algorithms for Compressed Videos --- p.19Chapter 2.2.1 --- Algorithms based on JPEG Image Sequences --- p.19Chapter 2.2.2 --- Algorithms based on MPEG Videos --- p.20Chapter 2.2.3 --- Algorithms based on VQ Compressed Videos --- p.21Chapter 2.3 --- Frame Difference Analysis Methods --- p.21Chapter 2.3.1 --- Scene Cut Detection --- p.21Chapter 2.3.2 --- Gradual Transition Detection --- p.22Chapter 2.4 --- Speedup Techniques --- p.23Chapter 2.5 --- Other Approaches --- p.24Chapter 3 --- Analysis and Enhancement of Existing Algorithms --- p.25Chapter 3.1 --- Introduction --- p.25Chapter 3.2 --- Video Segmentation Algorithms --- p.26Chapter 3.2.1 --- Frame Difference Metrics --- p.26Chapter 3.2.2 --- Frame Difference Analysis Methods --- p.29Chapter 3.3 --- Analysis of Feature Extraction Algorithms --- p.30Chapter 3.3.1 --- Pair-wise pixel comparison --- p.30Chapter 3.3.2 --- Color histogram comparison --- p.34Chapter 3.3.3 --- Pair-wise block-based comparison of DCT coefficients --- p.38Chapter 3.3.4 --- Pair-wise pixel comparison of DC-images --- p.42Chapter 3.4 --- Analysis of Scene Change Detection Methods --- p.45Chapter 3.4.1 --- Global Threshold Method --- p.45Chapter 3.4.2 --- Sliding Window Method --- p.46Chapter 3.5 --- Enhancements and Modifications --- p.47Chapter 3.5.1 --- Histogram Equalization --- p.49Chapter 3.5.2 --- DD Method --- p.52Chapter 3.5.3 --- LA Method --- p.56Chapter 3.5.4 --- Modification for pair-wise pixel comparison --- p.57Chapter 3.5.5 --- Modification for pair-wise DCT block comparison --- p.61Chapter 3.6 --- Conclusion --- p.69Chapter 4 --- Color Difference Histogram --- p.72Chapter 4.1 --- Introduction --- p.72Chapter 4.2 --- Color Difference Histogram --- p.73Chapter 4.2.1 --- Definition of Color Difference Histogram --- p.73Chapter 4.2.2 --- Sparse Distribution of CDH --- p.76Chapter 4.2.3 --- Resolution of CDH --- p.77Chapter 4.2.4 --- CDH-based Inter-frame Similarity Measure --- p.77Chapter 4.2.5 --- Computational Cost and Discriminating Power --- p.80Chapter 4.2.6 --- Suitability in Scene Change Detection --- p.83Chapter 4.3 --- Insensitivity to Illumination Changes --- p.89Chapter 4.3.1 --- Sensitivity of CDH --- p.90Chapter 4.3.2 --- Comparison with other feature extraction algorithms --- p.93Chapter 4.4 --- Orientation and Motion Invariant --- p.96Chapter 4.4.1 --- Camera Movements --- p.97Chapter 4.4.2 --- Object Motion --- p.100Chapter 4.4.3 --- Comparison with other feature extraction algorithms --- p.100Chapter 4.5 --- Performance of Scene Cut Detection --- p.102Chapter 4.6 --- Time Complexity Comparison --- p.105Chapter 4.7 --- Extension to DCT-compressed Images --- p.106Chapter 4.7.1 --- Performance of scene cut detection --- p.108Chapter 4.8 --- Conclusion --- p.109Chapter 5 --- Scene Change Detection --- p.111Chapter 5.1 --- Introduction --- p.111Chapter 5.2 --- Previous Approaches --- p.112Chapter 5.2.1 --- Scene Cut Detection --- p.112Chapter 5.2.2 --- Gradual Transition Detection --- p.115Chapter 5.3 --- DD Method --- p.116Chapter 5.3.1 --- Detecting Scene Cuts --- p.117Chapter 5.3.2 --- Detecting 1-frame Transitions --- p.121Chapter 5.3.3 --- Detecting Gradual Transitions --- p.129Chapter 5.4 --- Local Thresholding --- p.131Chapter 5.5 --- Experimental Results --- p.134Chapter 5.5.1 --- Performance of CDH+DD and CDH+DL --- p.135Chapter 5.5.2 --- Performance of DD on other features --- p.144Chapter 5.6 --- Conclusion --- p.150Chapter 6 --- Motion Vector Based Approach --- p.151Chapter 6.1 --- Introduction --- p.151Chapter 6.2 --- Previous Approaches --- p.152Chapter 6.3 --- MPEG-I Video Stream Format --- p.153Chapter 6.4 --- Derivation of Frame Differences from Motion Vector Counts --- p.156Chapter 6.4.1 --- Types of Frame Pairs --- p.156Chapter 6.4.2 --- Conditions for Scene Changes --- p.157Chapter 6.4.3 --- Frame Difference Measure --- p.159Chapter 6.5 --- Experiment --- p.160Chapter 6.5.1 --- Performance of MV --- p.161Chapter 6.5.2 --- Performance Enhancement --- p.162Chapter 6.5.3 --- Limitations --- p.163Chapter 6.6 --- Conclusion --- p.164Chapter 7 --- Conclusion and Future Work --- p.165Chapter 7.1 --- Contributions --- p.165Chapter 7.2 --- Future Work --- p.169Chapter 7.3 --- Conclusion --- p.171Bibliography --- p.174Chapter A --- Sample Videos --- p.180Chapter B --- List of Abbreviations --- p.18