Cross-layer Optimized Wireless Video Surveillance

A wireless video surveillance system contains three major components, the video capture and preprocessing, the video compression and transmission over wireless sensor networks (WSNs), and the video analysis at the receiving end. The coordination of different components is important for improving the end-to-end video quality, especially under the communication resource constraint. Cross-layer control proves to be an efficient measure for optimal system configuration. In this dissertation, we address the problem of implementing cross-layer optimization in the wireless video surveillance system. The thesis work is based on three research projects. In the first project, a single PTU (pan-tilt-unit) camera is used for video object tracking. The problem studied is how to improve the quality of the received video by jointly considering the coding and transmission process. The cross-layer controller determines the optimal coding and transmission parameters, according to the dynamic channel condition and the transmission delay. Multiple error concealment strategies are developed utilizing the special property of the PTU camera motion. In the second project, the binocular PTU camera is adopted for video object tracking. The presented work studied the fast disparity estimation algorithm and the 3D video transcoding over the WSN for real-time applications. The disparity/depth information is estimated in a coarse-to-fine manner using both local and global methods. The transcoding is coordinated by the cross-layer controller based on the channel condition and the data rate constraint, in order to achieve the best view synthesis quality. The third project is applied for multi-camera motion capture in remote healthcare monitoring. The challenge is the resource allocation for multiple video sequences. The presented cross-layer design incorporates the delay sensitive, content-aware video coding and transmission, and the adaptive video coding and transmission to ensure the optimal and balanced quality for the multi-view videos. In these projects, interdisciplinary study is conducted to synergize the surveillance system under the cross-layer optimization framework. Experimental results demonstrate the efficiency of the proposed schemes. The challenges of cross-layer design in existing wireless video surveillance systems are also analyzed to enlighten the future work. Adviser: Song C

Cross-layer Optimized Wireless Video Surveillance

A wireless video surveillance system contains three major components, the video capture and preprocessing, the video compression and transmission over wireless sensor networks (WSNs), and the video analysis at the receiving end. The coordination of different components is important for improving the end-to-end video quality, especially under the communication resource constraint. Cross-layer control proves to be an efficient measure for optimal system configuration. In this dissertation, we address the problem of implementing cross-layer optimization in the wireless video surveillance system. The thesis work is based on three research projects. In the first project, a single PTU (pan-tilt-unit) camera is used for video object tracking. The problem studied is how to improve the quality of the received video by jointly considering the coding and transmission process. The cross-layer controller determines the optimal coding and transmission parameters, according to the dynamic channel condition and the transmission delay. Multiple error concealment strategies are developed utilizing the special property of the PTU camera motion. In the second project, the binocular PTU camera is adopted for video object tracking. The presented work studied the fast disparity estimation algorithm and the 3D video transcoding over the WSN for real-time applications. The disparity/depth information is estimated in a coarse-to-fine manner using both local and global methods. The transcoding is coordinated by the cross-layer controller based on the channel condition and the data rate constraint, in order to achieve the best view synthesis quality. The third project is applied for multi-camera motion capture in remote healthcare monitoring. The challenge is the resource allocation for multiple video sequences. The presented cross-layer design incorporates the delay sensitive, content-aware video coding and transmission, and the adaptive video coding and transmission to ensure the optimal and balanced quality for the multi-view videos. In these projects, interdisciplinary study is conducted to synergize the surveillance system under the cross-layer optimization framework. Experimental results demonstrate the efficiency of the proposed schemes. The challenges of cross-layer design in existing wireless video surveillance systems are also analyzed to enlighten the future work. Adviser: Song C

A Framework of Video Bit-Stream Scalability and Efficient, Secure Social Video Sharing in the Clouds

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Analysis for Scalable Coding of Quality-Adjustable Sensor Data

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 신현식.Machine-generated data such as sensor data now comprise major portion of available information. This thesis addresses two important problems: storing of massive sensor data collection and efficient sensing. We first propose a quality-adjustable sensor data archiving, which compresses entire collection of sensor data efficiently without compromising key features. Considering the data aging aspect of sensor data, we make our archiving scheme capable of controlling data fidelity to exploit less frequent data access of user. This flexibility on quality adjustability leads to more efficient usage of storage space. In order to store data from various sensor types in cost-effective way, we study the optimal storage configuration strategy using analytical models that capture characteristics of our scheme. This strategy helps storing sensor data blocks with the optimal configurations that maximizes data fidelity of various sensor data under given storage space. Next, we consider efficient sensing schemes and propose a quality-adjustable sensing scheme. We adopt compressive sensing (CS) that is well suited for resource-limited sensors because of its low computational complexity. We enhance quality adjustability intrinsic to CS with quantization and especially temporal downsampling. Our sensing architecture provides more rate-distortion operating points than previous schemes, which enables sensors to adapt data quality in more efficient way considering overall performance. Moreover, the proposed temporal downsampling improves coding efficiency that is a drawback of CS. At the same time, the downsampling further reduces computational complexity of sensing devices, along with sparse random matrix. As a result, our quality-adjustable sensing can deliver gains to a wide variety of resource-constrained sensing techniques.Abstract i Contents iii List of Figures vi List of Tables x Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Spatio-Temporal Correlation in Sensor Data 3 1.3 Quality Adjustability of Sensor Data 7 1.4 Research Contributions 9 1.5 Thesis Organization 11 Chapter 2 Archiving of Sensor Data 12 2.1 Encoding Sensor Data Collection 12 2.1.1 Archiving Architecture 13 2.1.2 Data Conversion 16 2.2 Compression Ratio Comparison 20 2.3 Quality-Adjustable Archiving Model 25 2.3.1 Data Fidelity Model: Rate 25 2.3.2 Data Fidelity Model: Distortion 28 2.4 QP-Rate-Distortion Model 36 2.5 Optimal Rate Allocation 40 2.5.1 Rate Allocation Strategy 40 2.5.2 Optimal Storage Configuration 41 2.5.3 Experimental Results 44 Chapter 3 Scalable Management of Storage 46 3.1 Scalable Quality Management 46 3.1.1 Archiving Architecture 47 3.1.2 Compression Ratio Comparison 49 3.2 Enhancing Quality Adjustability 51 3.2.1 Data Fidelity Model: Rate 52 3.2.2 Data Fidelity Model: Distortion 55 3.3 Optimal Rate Allocation 59 3.3.1 Rate Allocation Strategy 60 3.3.2 Optimal Storage Configuration 63 3.3.3 Experimental Results 71 Chapter 4 Quality-Adjustable Sensing 73 4.1 Compressive Sensing 73 4.1.1 Compressive Sensing Problem 74 4.1.2 General Signal Recovery 76 4.1.3 Noisy Signal Recovery 76 4.2 Quality Adjustability in Sensing Environment 77 4.2.1 Quantization and Temporal Downsampling 79 4.2.2 Optimization with Error Model 85 4.3 Low-Complexity Sensing 88 4.3.1 Sparse Random Matrix 89 4.3.2 Resource Savings 92 Chapter 5 Conclusions 96 5.1 Summary 96 5.2 Future Research Directions 98 Bibliography 100 Abstract in Korean 109Docto

Architectures for Adaptive Low-Power Embedded Multimedia Systems

This Ph.D. thesis describes novel hardware/software architectures for adaptive low-power embedded multimedia systems. Novel techniques for run-time adaptive energy management are proposed, such that both HW & SW adapt together to react to the unpredictable scenarios. A complete power-aware H.264 video encoder was developed. Comparison with state-of-the-art demonstrates significant energy savings while meeting the performance constraint and keeping the video quality degradation unnoticeable