Cooperative Visual Monitoring in Energy- Constrained Wireless Sensor Networks
|
|
- Cameron Merritt
- 5 years ago
- Views:
Transcription
1 Cooperative Visual Monitoring in Energy- Constrained Wireless Sensor Networks Zichong CHEN AudioVisual Communications Laboratory École Polytechnique Fédérale de Lausanne (EPFL), Switzerland Thesis supervisor: Prof. Martin Vetterli Thesis co-supervisor: Dr. Guillermo Barrenetxea
2 Outline Motivation Cooperative sampling framework Cooperative coding for distributed image sources Cooperative coding for distributed video sources Event-driven video coding using smart cameras Conclusions and future directions 2
3 Wireless visual monitoring CMOS cameras are cheap, emerge in wireless sensor networks (WSNs) Visual access is helpful for monitoring applications: BirdNest [Hicks2008] Environmental monitoring: e.g., avalanche detection The Swiss environmental research using Sensorscope wireless sensor networks [Ingelrest2010] 3
4 Why camera network? Robustness Risk imposed by the surrounding environment: strong winds, avalanches, or rock slides In surveillance applications, the intruders can destroy monitoring cameras on purpose One camera is not enough! 4
5 Why camera network? Energy constraint Power source: energy-harvesting solar cells WSN node is becoming smaller and cheaper, hence solar cells must shrink in size Using Motion JPEG, the energy constraint only allows for transmitting less than two frames every minute Multiple cameras can share the energy burden! 5
6 What this thesis is about? How to design an efficient multi-camera monitoring system Base station (BS) Cameras Event Detected scene Time Camera failure Sampling Problem (Part 1): how to coordinate multiple cameras for an optimal event-detection probability? Coding Problem (Part 2-4): how to exploit signal statistics locally and across different cameras for reducing communication costs? 6
7 Outline Motivation Cooperative sampling framework Cooperative coding for distributed image sources Cooperative coding for distributed video sources Event-driven video coding using smart cameras Conclusions and future directions 7
8 Interleaved sampling Two cameras with overlapped views, high redundancy!! Target: maximize the probability to catch a random event?. Camera Camera 2 τ 2/f EVENT len: Δ Time Optimal strategy: two cameras sample in a symmetric interleaved manner Time Camera 1 Camera 2 8
9 How to synchronize the network To form the interleaved sampling configuration A distributed synchronization mechanism that adapts to clock drift, camera arrival/loss How: overhearing Tx a beacon when sampling Broadcast graph 9
10 Self-organized sampling (1) DESYNC Algorithm [Degesys2007]: corrected Tx scheduled Tx Time T Sampling interval of each camera Converge exponentially if there is a Hamiltonian cycle E.g., a fully connected network
11 Self-organized sampling (2) A partially connected network: Find a Hamiltonian cycle (if exists) Hamiltonian 1 2 Non-Hamiltonian Three components in addition to DESYNC Find Hamiltonian cycle [Levy2005]: linear time on dense network Coordinate initial schedule order (otherwise converge to sth. else) Recovering algorithms for camera failure or merging 11
12 How to deploy cameras? Constraint: the broadcast graph must be Hamiltonian Option A: to place cameras evenly in a circle around the area of interest Area of Interest BS Option B: the radio transmission range of each camera should cover at least half of the network 12
13 Outline Motivation Cooperative sampling framework Cooperative coding for distributed image sources Cooperative coding for distributed video sources Event-driven video coding using smart cameras Conclusions and future directions 13
14 CHANNEL time Cooperative Visual Monitoring in Energy-Constrained Wireless Sensor Networks Cooperative coding of multi-view images Transmit the stereo-view images X and Y to BS DiSAC2: Split the coding procedure into successive phases imitate the interleaved sampling configuration X ENCx C 1 BS (X 1, Y 1 ) Y ENCy C 2 BS (X 2, Y 2 ) X ENCx C 3 BS (X 3, Y 3 ) Y ENCy C 4 BS (X 4, Y 4 ) 14
15 Related to successive refinement theory Single Encoder Case: Decoder gets better image quality step by step using successive descriptions D scene camera D 1 D 1 R 1 additional R 2 D 2 R 1 R 1 +R 2 R D 2 OR R 1 +R 2 For Gaussian source, the {rate, distortion} operates on the R(D) curve at each stage. [Equitz1991] 15
16 DiSAC2 with Gaussian model X R 1 additional R 3 broadcast Y additional R 2 scene two cameras X 1 Y 1 X 2 Y 2 X 3 Y 3 We prove: For Gaussian sources, the {sum-rate, distortion pair} operates on the Wagner Surface* at each stage, for any rate combinations (R 1, R 2, R 3, ). * A. B. Wagner, S. Tavildar, and P. Viswanath, Rate region of the quadratic gaussian two-encoder source-coding problem, IEEE Trans. Inf. Theory, vol. 54, no. 5, pp ,
17 DiSAC2 with real images We combine layer decomposition and linear prediction method Two dataset: Church, Park X: Y: X 1 Y 2 X 3 Y 4 Simulation results Surfaces: centralized coding as a bound of distributed coding Curves: operating points of DiSAC2 Within 3dB to the centralized coding bound Church Park 17
18 Outline Motivation Cooperative sampling framework Cooperative coding for distributed image sources Cooperative coding for distributed video sources Event-driven video coding using smart cameras Conclusions and future directions 18
19 Video coding under interleaved sampling Videos from multiple cameras actually form a single video sequence. Camera 1 Camera 2 Independent video coding: each camera does its own job Merge 1/f Time 19
20 Video coding under interleaved sampling Videos from multiple cameras actually form a single video sequence. Camera 1 Camera 2 Each frame encoded indep. Independent video coding: each camera does its own job Merge 1/f Time Distributed video coding: DISCOVER [Girod2005] No comm. needed between two cameras Wyner-Ziv frame Quantizer Slepian- Wolf Encoder Slepian- Wolf Decoder Reconstruction Multiplexer Keyframe Encoder Intra-frame Encoder Intra-frame Decoder Decoder Side information 20
21 Video coding under interleaved sampling Videos from multiple cameras actually form a single video sequence. Camera 1 Camera 2 Independent video coding: each camera does its own job Merge 1/f Time Distributed video coding: DISCOVER [Girod2005] Joint video coding: overhearing is cheap in long range communication + _ Color Conv. DCT camera Q 1 IQ 2 Entropy encoder Entropy decoder Tx Rx antenna + IDCT + Switch to 1: Transmitting, Tx on, Rx off. Switch to 2: Overhearing, Tx off, Rx on. [Wang2006] P 21
22 Experimental setup Two regular cameras capture the datasets, and run algorithm on Sensorcam smart camera platform to get approximate energy profile facade of a building: planar scene suitable for image registration complex depth structure : mimicking two cameras deployed in a distributed manner Algorithm implementation written in C, based on OpenCV and x264 libraries Use computation time to estimate computation consumption Use compression ratio to estimate communication consumption 22
23 Exp.1: Which video coding scheme is better? Distributed coding and H.264 based joint coding Distributed coding fail to exploit correlation between cameras Sampling rate: <0.02 fps in environmental monitoring versus 30 fps in regular video Registration errors between cameras 23
24 Exp.1: Which video coding scheme is better? H.264 based joint coding and H.264 based independent coding Energy saving of joint coding in the 2Dparameter space of (GOP, sampling interval) Group of Pictures (GOP): T GOP=4 Error-resilience capability: GOP*T Time Joint coding works better when the sampling rate is low GOP is smaller than conventional videos a constant errorresilience capability of 60min Scene A Scene B 24
25 Exp.2: Per-camera consumption Solid lines: Per-camera real-time consumption of a two-camera system using joint coding Dashed line: Real-time consumption of a single-camera system with the same event detection probability Per-camera consumption is reduced by 30%-50%, consistent with a factor of camera number 2 Performance loss: Registration error and overhearing cost 25
26 Outline Motivation Cooperative sampling framework Cooperative coding for distributed image sources Cooperative coding for distributed video sources Event-driven video coding using smart cameras Conclusions and future directions 26
27 What we really care in monitoring tasks? Relevant: people, cars, explosion Irrelevant: cloud, light, shadows Irrelevant Relevant Video codec like H.264 is inefficient as it ignore the ``meaning'' of video content! 27
28 To make camera smarter Wireless cameras nowadays are smart: Processing before transmitting When processing cost is smaller than the reduced communication cost Anomaly detection [Chandola2009] based on pattern recognition Does work 100%: missing many Need to collect events training data for each application 28
29 Our scheme: Event-driven video coding (EVC) Locate salient pixels in each image frame, and transmit the image fragments marked with saliency Refreshing: Group of pictures (GOP) Too much blocks detected as salient Decoding: an image blending problem Pyramid blending Poisson image editing 29
30 Simulation result: Compression rate Same quality on the detected events 70% energy saving over H.264! 30
31 Simulation result: Detection rate Compare with Anomaly detection based video coding (AnVC) Define an event as the appearance of one person 31
32 Implementation on a real wireless camera Monitor a parking lot using a smart camera Power management: wake-up; take an image and transmit; sleep until next schedule An energy meter connected in the power source to monitor the overall energy 40% energy saving over H.264! 32
33 EVC under interleaved sampling A large scale dataset with nine cameras 33
34 EVC under interleaved sampling Scaling behavior of per-camera communication cost Ideally 1/n Interleaved sampling configuration: 1 Camera 1 T s Time 2 Cameras Experiment result 1 2 2T s Time 3 Cameras 1 2 3T s Time 3 Due to increasing interframe variance Analysis with Gaussian model supports the loss term O(log n/n) 34
35 Multi-camera EVC 3 Cameras phantom frame regular frame T s Experiment result T s The loss term is eliminated Time Each camera takes a phantom frame Cost of taking an image is small Phantom frame s event is sent by other cameras Use it in saliency detection 35
36 Outline Motivation Cooperative sampling framework Cooperative coding for distributed image sources Cooperative coding for distributed video sources Event-driven video coding using smart cameras Conclusions and future directions 36
37 Conclusions and future research Cooperative sampling framework for a multi-camera monitoring system Increase system robustness Reduce per-camera energy burden Coding methods for multiple cameras under the proposed sampling configuration Multi-view images Multi-view videos Adapt event-driven coding scheme Future research Fusion of information from many cameras with limited overlapping views MIMO can reduce energy consumption even further 37
38 Reference J. Hicks et al. (2008), An easily deployable wireless imaging system, Proceedings of the Workshop on Applications, Systems, and Algorithms for Image Sensing F. Ingelrest et al. (2010), SensorScope: Application-specific sensor network for environmental monitoring, ACM Transactions on Sensor Networks Ore, Ø. (1960), Note on Hamilton circuits, American Mathematical Monthly W. Equitz and T. Cover (1991), Successive refinement of information, IEEE Transactions on Information Theory B. Girod et al. (2005). Distributed video coding, Proc. IEEE A. Wang and C. Sodini (2006). On the energy efficiency of wireless transceivers, Proc. ICC V. Chandola et al. (2009), Anomaly detection: A survey, ACM Computing Surveys J. Degesys et al. (2007), DESYNC: Self-Organizing Desynchronization and TDMA on Wireless Sensor Networks, Proc. IPSN Levy et al. (2005), A distributed algorithm to find hamiltonian cycles in g(np) random graphs," Proc. CAAN 38
39 Thank you! Questions? Twelve Stereo Cameras - pottery figurines of Chinese zodiac animal (AD )
Robust Video Coding. Heechan Park. Signal and Image Processing Group Computer Science Department University of Warwick. for CS403
Robust Video Coding for CS403 Heechan Park Signal and Image Processing Group Computer Science Department University of Warwick Standard Video Coding Scalable Video Coding Distributed Video Coding Video
More informationDistributed Video Coding
Distributed Video Coding Bernd Girod Anne Aaron Shantanu Rane David Rebollo-Monedero David Varodayan Information Systems Laboratory Stanford University Outline Lossless and lossy compression with receiver
More informationFrequency Band Coding Mode Selection for Key Frames of Wyner-Ziv Video Coding
2009 11th IEEE International Symposium on Multimedia Frequency Band Coding Mode Selection for Key Frames of Wyner-Ziv Video Coding Ghazaleh R. Esmaili and Pamela C. Cosman Department of Electrical and
More informationResearch on Distributed Video Compression Coding Algorithm for Wireless Sensor Networks
Sensors & Transducers 203 by IFSA http://www.sensorsportal.com Research on Distributed Video Compression Coding Algorithm for Wireless Sensor Networks, 2 HU Linna, 2 CAO Ning, 3 SUN Yu Department of Dianguang,
More informationCMPT 365 Multimedia Systems. Media Compression - Video
CMPT 365 Multimedia Systems Media Compression - Video Spring 2017 Edited from slides by Dr. Jiangchuan Liu CMPT365 Multimedia Systems 1 Introduction What s video? a time-ordered sequence of frames, i.e.,
More informationMultiple-View Object Recognition in Band-Limited Distributed Camera Networks
in Band-Limited Distributed Camera Networks Allen Y. Yang, Subhransu Maji, Mario Christoudas, Kirak Hong, Posu Yan Trevor Darrell, Jitendra Malik, and Shankar Sastry Fusion, 2009 Classical Object Recognition
More informationLOW DELAY DISTRIBUTED VIDEO CODING. António Tomé. Instituto Superior Técnico Av. Rovisco Pais, Lisboa, Portugal
LOW DELAY DISTRIBUTED VIDEO CODING António Tomé Instituto Superior Técnico Av. Rovisco Pais, 1049-001 Lisboa, Portugal E-mail: MiguelSFT@gmail.com Abstract Distributed Video Coding (DVC) is a new video
More informationCompressive Sensing for Multimedia. Communications in Wireless Sensor Networks
Compressive Sensing for Multimedia 1 Communications in Wireless Sensor Networks Wael Barakat & Rabih Saliba MDDSP Project Final Report Prof. Brian L. Evans May 9, 2008 Abstract Compressive Sensing is an
More informationIEEE 1857 Standard Empowering Smart Video Surveillance Systems
IEEE 1857 Standard Empowering Smart Video Surveillance Systems Wen Gao, Yonghong Tian, Tiejun Huang, Siwei Ma, Xianguo Zhang to be published in IEEE Intelligent Systems (released in 2013). Effrosyni Doutsi
More informationModule 7 VIDEO CODING AND MOTION ESTIMATION
Module 7 VIDEO CODING AND MOTION ESTIMATION Lesson 20 Basic Building Blocks & Temporal Redundancy Instructional Objectives At the end of this lesson, the students should be able to: 1. Name at least five
More informationReview and Implementation of DWT based Scalable Video Coding with Scalable Motion Coding.
Project Title: Review and Implementation of DWT based Scalable Video Coding with Scalable Motion Coding. Midterm Report CS 584 Multimedia Communications Submitted by: Syed Jawwad Bukhari 2004-03-0028 About
More informationRate Distortion Optimization in Video Compression
Rate Distortion Optimization in Video Compression Xue Tu Dept. of Electrical and Computer Engineering State University of New York at Stony Brook 1. Introduction From Shannon s classic rate distortion
More informationVideo Compression Method for On-Board Systems of Construction Robots
Video Compression Method for On-Board Systems of Construction Robots Andrei Petukhov, Michael Rachkov Moscow State Industrial University Department of Automatics, Informatics and Control Systems ul. Avtozavodskaya,
More informationDegrees of Freedom in Cached Interference Networks with Limited Backhaul
Degrees of Freedom in Cached Interference Networks with Limited Backhaul Vincent LAU, Department of ECE, Hong Kong University of Science and Technology (A) Motivation Interference Channels 3 No side information
More informationVideo Compression An Introduction
Video Compression An Introduction The increasing demand to incorporate video data into telecommunications services, the corporate environment, the entertainment industry, and even at home has made digital
More informationNetwork Image Coding for Multicast
Network Image Coding for Multicast David Varodayan, David Chen and Bernd Girod Information Systems Laboratory, Stanford University Stanford, California, USA {varodayan, dmchen, bgirod}@stanford.edu Abstract
More informationStereo Image Compression
Stereo Image Compression Deepa P. Sundar, Debabrata Sengupta, Divya Elayakumar {deepaps, dsgupta, divyae}@stanford.edu Electrical Engineering, Stanford University, CA. Abstract In this report we describe
More information6th International Workshop on OMNeT++
6th International Workshop on OMNeT++ An OMNeT++ Framework to Evaluate Video Transmission in Mobile Wireless Multimedia Sensor Networks Denis Rosário, Zhongliang Zhao, Claudio Silva, Eduardo Cerqueira,
More informationCompression of Stereo Images using a Huffman-Zip Scheme
Compression of Stereo Images using a Huffman-Zip Scheme John Hamann, Vickey Yeh Department of Electrical Engineering, Stanford University Stanford, CA 94304 jhamann@stanford.edu, vickey@stanford.edu Abstract
More informationQUANTIZER DESIGN FOR EXPLOITING COMMON INFORMATION IN LAYERED CODING. Mehdi Salehifar, Tejaswi Nanjundaswamy, and Kenneth Rose
QUANTIZER DESIGN FOR EXPLOITING COMMON INFORMATION IN LAYERED CODING Mehdi Salehifar, Tejaswi Nanjundaswamy, and Kenneth Rose Department of Electrical and Computer Engineering University of California,
More informationUpcoming Video Standards. Madhukar Budagavi, Ph.D. DSPS R&D Center, Dallas Texas Instruments Inc.
Upcoming Video Standards Madhukar Budagavi, Ph.D. DSPS R&D Center, Dallas Texas Instruments Inc. Outline Brief history of Video Coding standards Scalable Video Coding (SVC) standard Multiview Video Coding
More informationMeasurements and Bits: Compressed Sensing meets Information Theory. Dror Baron ECE Department Rice University dsp.rice.edu/cs
Measurements and Bits: Compressed Sensing meets Information Theory Dror Baron ECE Department Rice University dsp.rice.edu/cs Sensing by Sampling Sample data at Nyquist rate Compress data using model (e.g.,
More informationStudy and Development of Image Authentication using Slepian Wolf Coding
e t International Journal on Emerging Technologies 6(2): 126-130(2015) ISSN No. (Print) : 0975-8364 ISSN No. (Online) : 2249-3255 Study and Development of Image Authentication using Slepian Wolf Coding
More informationTime Synchronization in Wireless Sensor Networks: CCTS
Time Synchronization in Wireless Sensor Networks: CCTS 1 Nerin Thomas, 2 Smita C Thomas 1, 2 M.G University, Mount Zion College of Engineering, Pathanamthitta, India Abstract: A time synchronization algorithm
More informationJPEG 2000 vs. JPEG in MPEG Encoding
JPEG 2000 vs. JPEG in MPEG Encoding V.G. Ruiz, M.F. López, I. García and E.M.T. Hendrix Dept. Computer Architecture and Electronics University of Almería. 04120 Almería. Spain. E-mail: vruiz@ual.es, mflopez@ace.ual.es,
More informationRate-distortion Optimized Streaming of Compressed Light Fields with Multiple Representations
Rate-distortion Optimized Streaming of Compressed Light Fields with Multiple Representations Prashant Ramanathan and Bernd Girod Department of Electrical Engineering Stanford University Stanford CA 945
More informationImage Segmentation Techniques for Object-Based Coding
Image Techniques for Object-Based Coding Junaid Ahmed, Joseph Bosworth, and Scott T. Acton The Oklahoma Imaging Laboratory School of Electrical and Computer Engineering Oklahoma State University {ajunaid,bosworj,sacton}@okstate.edu
More informationDIGITAL TELEVISION 1. DIGITAL VIDEO FUNDAMENTALS
DIGITAL TELEVISION 1. DIGITAL VIDEO FUNDAMENTALS Television services in Europe currently broadcast video at a frame rate of 25 Hz. Each frame consists of two interlaced fields, giving a field rate of 50
More informationRate-distortion Optimized Streaming of Compressed Light Fields with Multiple Representations
Rate-distortion Optimized Streaming of Compressed Light Fields with Multiple Representations Prashant Ramanathan and Bernd Girod Department of Electrical Engineering Stanford University Stanford CA 945
More informationRegion-based Fusion Strategy for Side Information Generation in DMVC
Region-based Fusion Strategy for Side Information Generation in DMVC Yongpeng Li * a, Xiangyang Ji b, Debin Zhao c, Wen Gao d a Graduate University, Chinese Academy of Science, Beijing, 39, China; b Institute
More informationMulti-View Image Coding in 3-D Space Based on 3-D Reconstruction
Multi-View Image Coding in 3-D Space Based on 3-D Reconstruction Yongying Gao and Hayder Radha Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48823 email:
More informationMotion Estimation for Video Coding Standards
Motion Estimation for Video Coding Standards Prof. Ja-Ling Wu Department of Computer Science and Information Engineering National Taiwan University Introduction of Motion Estimation The goal of video compression
More informationVery Low Bit Rate Color Video
1 Very Low Bit Rate Color Video Coding Using Adaptive Subband Vector Quantization with Dynamic Bit Allocation Stathis P. Voukelatos and John J. Soraghan This work was supported by the GEC-Marconi Hirst
More informationCooperative Wireless Communications. Ashutosh Sabharwal
Cooperative Wireless Communications Ashutosh Sabharwal Outline Growing presence of wireless Why do we need a new paradigm Cooperative communication basics Future directions In Numbers Global cellular phone
More information4G WIRELESS VIDEO COMMUNICATIONS
4G WIRELESS VIDEO COMMUNICATIONS Haohong Wang Marvell Semiconductors, USA Lisimachos P. Kondi University of Ioannina, Greece Ajay Luthra Motorola, USA Song Ci University of Nebraska-Lincoln, USA WILEY
More informationECE 417 Guest Lecture Video Compression in MPEG-1/2/4. Min-Hsuan Tsai Apr 02, 2013
ECE 417 Guest Lecture Video Compression in MPEG-1/2/4 Min-Hsuan Tsai Apr 2, 213 What is MPEG and its standards MPEG stands for Moving Picture Expert Group Develop standards for video/audio compression
More informationDistributed Source Coding for Image and Video Applications. Acknowledgements
Distributed Source oding for Image and Video Applications Ngai-Man (Man) HEUNG Signal and Image Processing Institute University of Southern alifornia http://biron.usc.edu/~ncheung/ 1 Acknowledgements ollaborators
More informationLecture 7, Video Coding, Motion Compensation Accuracy
Lecture 7, Video Coding, Motion Compensation Accuracy Last time we saw several methods to obtain a good motion estimation, with reduced complexity (efficient search), and with the possibility of sub-pixel
More informationIMAGE COMPRESSION. Image Compression. Why? Reducing transportation times Reducing file size. A two way event - compression and decompression
IMAGE COMPRESSION Image Compression Why? Reducing transportation times Reducing file size A two way event - compression and decompression 1 Compression categories Compression = Image coding Still-image
More informationView Synthesis for Multiview Video Compression
View Synthesis for Multiview Video Compression Emin Martinian, Alexander Behrens, Jun Xin, and Anthony Vetro email:{martinian,jxin,avetro}@merl.com, behrens@tnt.uni-hannover.de Mitsubishi Electric Research
More informationXiaoqing Zhu, Sangeun Han and Bernd Girod Information Systems Laboratory Department of Electrical Engineering Stanford University
Congestion-aware Rate Allocation For Multipath Video Streaming Over Ad Hoc Wireless Networks Xiaoqing Zhu, Sangeun Han and Bernd Girod Information Systems Laboratory Department of Electrical Engineering
More informationTarget Tracking in Wireless Sensor Network
International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 6 (2014), pp. 643-648 International Research Publications House http://www. irphouse.com Target Tracking in
More informationNodes Energy Conserving Algorithms to prevent Partitioning in Wireless Sensor Networks
IJCSNS International Journal of Computer Science and Network Security, VOL.17 No.9, September 2017 139 Nodes Energy Conserving Algorithms to prevent Partitioning in Wireless Sensor Networks MINA MAHDAVI
More informationISSN: An Efficient Fully Exploiting Spatial Correlation of Compress Compound Images in Advanced Video Coding
An Efficient Fully Exploiting Spatial Correlation of Compress Compound Images in Advanced Video Coding Ali Mohsin Kaittan*1 President of the Association of scientific research and development in Iraq Abstract
More informationCompression Algorithms for Flexible Video Decoding
Compression Algorithms for Flexible Video Decoding Ngai-Man Cheung and Antonio Ortega Signal and Image Processing Institute, Univ. of Southern California, Los Angeles, CA ABSTRACT We investigate compression
More informationUniversity of Bristol - Explore Bristol Research. Peer reviewed version. Link to published version (if available): /ICIP.1996.
Redmill, DW., & Bull, DR. (1996). Error resilient arithmetic coding of still images. In Unknown (Vol. 2, pp. 109 112). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/icip.1996.560614
More informationLuca Schenato Workshop on cooperative multi agent systems Pisa, 6/12/2007
Distributed consensus protocols for clock synchronization in sensor networks Luca Schenato Workshop on cooperative multi agent systems Pisa, 6/12/2007 Outline Motivations Intro to consensus algorithms
More informationCompression of Light Field Images using Projective 2-D Warping method and Block matching
Compression of Light Field Images using Projective 2-D Warping method and Block matching A project Report for EE 398A Anand Kamat Tarcar Electrical Engineering Stanford University, CA (anandkt@stanford.edu)
More informationA Comprehensive Review of Distributed Coding Algorithms for Visual Sensor Network (VSN)
104 A Comprehensive Review of Distributed Coding Algorithms for Visual Sensor Network (VSN) Mansoor Ebrahim, Chai Wai Chong Faculty of Science & Technology, Sunway University, Selangor, Malaysia 12032389@imail.sunway.edu.my,
More informationChapter 11.3 MPEG-2. MPEG-2: For higher quality video at a bit-rate of more than 4 Mbps Defined seven profiles aimed at different applications:
Chapter 11.3 MPEG-2 MPEG-2: For higher quality video at a bit-rate of more than 4 Mbps Defined seven profiles aimed at different applications: Simple, Main, SNR scalable, Spatially scalable, High, 4:2:2,
More informationChapter 10. Basic Video Compression Techniques Introduction to Video Compression 10.2 Video Compression with Motion Compensation
Chapter 10 Basic Video Compression Techniques 10.1 Introduction to Video Compression 10.2 Video Compression with Motion Compensation 10.3 Search for Motion Vectors 10.4 H.261 10.5 H.263 10.6 Further Exploration
More informationMultimedia Systems Video II (Video Coding) Mahdi Amiri April 2012 Sharif University of Technology
Course Presentation Multimedia Systems Video II (Video Coding) Mahdi Amiri April 2012 Sharif University of Technology Video Coding Correlation in Video Sequence Spatial correlation Similar pixels seem
More informationAn Energy-Efficient Hierarchical Routing for Wireless Sensor Networks
Volume 2 Issue 9, 213, ISSN-2319-756 (Online) An Energy-Efficient Hierarchical Routing for Wireless Sensor Networks Nishi Sharma Rajasthan Technical University Kota, India Abstract: The popularity of Wireless
More informationEnergy Conservation through Sleep Scheduling in Wireless Sensor Network 1. Sneha M. Patil, Archana B. Kanwade 2
Energy Conservation through Sleep Scheduling in Wireless Sensor Network 1. Sneha M. Patil, Archana B. Kanwade 2 1 Student Department of Electronics & Telecommunication, SITS, Savitribai Phule Pune University,
More informationHomogeneous Transcoding of HEVC for bit rate reduction
Homogeneous of HEVC for bit rate reduction Ninad Gorey Dept. of Electrical Engineering University of Texas at Arlington Arlington 7619, United States ninad.gorey@mavs.uta.edu Dr. K. R. Rao Fellow, IEEE
More informationOptimized Analog Mappings for Distributed Source-Channel Coding
21 Data Compression Conference Optimized Analog Mappings for Distributed Source-Channel Coding Emrah Akyol, Kenneth Rose Dept. of Electrical & Computer Engineering University of California, Santa Barbara,
More informationVideo-Aware Wireless Networks (VAWN) Final Meeting January 23, 2014
Video-Aware Wireless Networks (VAWN) Final Meeting January 23, 2014 1/26 ! Real-time Video Transmission! Challenges and Opportunities! Lessons Learned for Real-time Video! Mitigating Losses in Scalable
More informationCompression of RADARSAT Data with Block Adaptive Wavelets Abstract: 1. Introduction
Compression of RADARSAT Data with Block Adaptive Wavelets Ian Cumming and Jing Wang Department of Electrical and Computer Engineering The University of British Columbia 2356 Main Mall, Vancouver, BC, Canada
More informationComplexity Reduced Mode Selection of H.264/AVC Intra Coding
Complexity Reduced Mode Selection of H.264/AVC Intra Coding Mohammed Golam Sarwer 1,2, Lai-Man Po 1, Jonathan Wu 2 1 Department of Electronic Engineering City University of Hong Kong Kowloon, Hong Kong
More informationA Image Comparative Study using DCT, Fast Fourier, Wavelet Transforms and Huffman Algorithm
International Journal of Engineering Research and General Science Volume 3, Issue 4, July-August, 15 ISSN 91-2730 A Image Comparative Study using DCT, Fast Fourier, Wavelet Transforms and Huffman Algorithm
More informationDesign and Evaluation of the Ultra- Reliable Low-Latency Wireless Protocol EchoRing
Design and Evaluation of the Ultra- Reliable Low-Latency Wireless Protocol EchoRing BWRC, September 22 nd 2017 joint work with C. Dombrowski, M. Serror, Y. Hu, S. Junges Machine-Type Communications: Origins
More informationEnergy-Efficient Communication Protocol for Wireless Micro-sensor Networks
Energy-Efficient Communication Protocol for Wireless Micro-sensor Networks Paper by: Wendi Rabiner Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan Outline Brief Introduction on Wireless Sensor
More informationPart I: Introduction to Wireless Sensor Networks. Xenofon Fafoutis
Part I: Introduction to Wireless Sensor Networks Xenofon Fafoutis Sensors 2 DTU Informatics, Technical University of Denmark Wireless Sensor Networks Sink Sensor Sensed Area 3 DTU Informatics,
More informationRobotics Programming Laboratory
Chair of Software Engineering Robotics Programming Laboratory Bertrand Meyer Jiwon Shin Lecture 8: Robot Perception Perception http://pascallin.ecs.soton.ac.uk/challenges/voc/databases.html#caltech car
More informationMultimedia Communication in Wireless Sensor Networks
1 Multimedia Communication in Wireless Sensor Networks Eren Gürses Özgür B. Akan Department of Electrical and Electronics Engineering Middle East Technical University, Ankara, Turkey, 06531 Tel: +90-(312)-210
More informationDalimir Orfanus (IFI UiO + ABB CRC), , Cyber Physical Systems Clustering in Wireless Sensor Networks 2 nd part : Examples
Dalimir Orfanus (IFI UiO + ABB CRC), 27.10.2011, Cyber Physical Systems Clustering in Wireless Sensor Networks 2 nd part : Examples Clustering in Wireless Sensor Networks Agenda LEACH Energy efficient
More informationOptimizing the Deblocking Algorithm for. H.264 Decoder Implementation
Optimizing the Deblocking Algorithm for H.264 Decoder Implementation Ken Kin-Hung Lam Abstract In the emerging H.264 video coding standard, a deblocking/loop filter is required for improving the visual
More informationWZS: WYNER-ZIV SCALABLE PREDICTIVE VIDEO CODING. Huisheng Wang, Ngai-Man Cheung and Antonio Ortega
WZS: WYNERZIV SCALABLE PREDICTIVE VIDEO CODING Huisheng Wang, NgaiMan Cheung and Antonio Ortega Integrated Media Systems Center and Department of Electrical Engineering University of Southern California,
More informationFundamentals of Video Compression. Video Compression
Fundamentals of Video Compression Introduction to Digital Video Basic Compression Techniques Still Image Compression Techniques - JPEG Video Compression Introduction to Digital Video Video is a stream
More informationPervasive Computing. OpenLab Jan 14 04pm L Institute of Networked and Embedded Systems
Pervasive Computing Institute of Networked and Embedded Systems OpenLab 2010 Jan 14 04pm L4.1.01 MISSION STATEMENT Founded in 2007, the Pervasive Computing Group at Klagenfurt University is part of the
More informationEnergy Management Issue in Ad Hoc Networks
Wireless Ad Hoc and Sensor Networks - Energy Management Outline Energy Management Issue in ad hoc networks WS 2010/2011 Main Reasons for Energy Management in ad hoc networks Classification of Energy Management
More informationA LOW-COMPLEXITY AND LOSSLESS REFERENCE FRAME ENCODER ALGORITHM FOR VIDEO CODING
2014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP) A LOW-COMPLEXITY AND LOSSLESS REFERENCE FRAME ENCODER ALGORITHM FOR VIDEO CODING Dieison Silveira, Guilherme Povala,
More informationGeorgios Tziritas Computer Science Department
New Video Coding standards MPEG-4, HEVC Georgios Tziritas Computer Science Department http://www.csd.uoc.gr/~tziritas 1 MPEG-4 : introduction Motion Picture Expert Group Publication 1998 (Intern. Standardization
More informationData Hiding in Video
Data Hiding in Video J. J. Chae and B. S. Manjunath Department of Electrical and Computer Engineering University of California, Santa Barbara, CA 9316-956 Email: chaejj, manj@iplab.ece.ucsb.edu Abstract
More informationCoding for the Network: Scalable and Multiple description coding Marco Cagnazzo
Coding for the Network: Scalable and Multiple description coding Marco Cagnazzo Overview Examples and motivations Scalable coding for network transmission Techniques for multiple description coding 2 27/05/2013
More informationImage Interpolation with Dense Disparity Estimation in Multiview Distributed Video Coding
Image Interpolation with Dense in Multiview Distributed Video Coding Wided Miled, Thomas Maugey, Marco Cagnazzo, Béatrice Pesquet-Popescu Télécom ParisTech, TSI departement, 46 rue Barrault 75634 Paris
More informationTri-modal Human Body Segmentation
Tri-modal Human Body Segmentation Master of Science Thesis Cristina Palmero Cantariño Advisor: Sergio Escalera Guerrero February 6, 2014 Outline 1 Introduction 2 Tri-modal dataset 3 Proposed baseline 4
More informationEnergy Management Issue in Ad Hoc Networks
Wireless Ad Hoc and Sensor Networks (Energy Management) Outline Energy Management Issue in ad hoc networks WS 2009/2010 Main Reasons for Energy Management in ad hoc networks Classification of Energy Management
More informationVIDEO COMPRESSION STANDARDS
VIDEO COMPRESSION STANDARDS Family of standards: the evolution of the coding model state of the art (and implementation technology support): H.261: videoconference x64 (1988) MPEG-1: CD storage (up to
More informationMultihop Hierarchical MIMO A Multicast Structure in wireless ad hoc networks
Multihop Hierarchical MIMO A Multicast Structure in wireless ad hoc networks January 11, 2008 Abstract In this paper, we study multicast in large-scale wireless ad hoc networks. Consider N nodes that are
More informationData Compression Algorithm for Wireless Sensor Network
Data Compression Algorithm for Wireless Sensor Network Reshma B. Bhosale 1, Rupali R. Jagtap 2 1,2 Department of Electronics & Telecommunication Engineering, Annasaheb Dange College of Engineering & Technology,
More informationEnd-To-End Delay Optimization in Wireless Sensor Network (WSN)
Shweta K. Kanhere 1, Mahesh Goudar 2, Vijay M. Wadhai 3 1,2 Dept. of Electronics Engineering Maharashtra Academy of Engineering, Alandi (D), Pune, India 3 MITCOE Pune, India E-mail: shweta.kanhere@gmail.com,
More informationComputer Based Image Algorithm For Wireless Sensor Networks To Prevent Hotspot Locating Attack
Computer Based Image Algorithm For Wireless Sensor Networks To Prevent Hotspot Locating Attack J.Anbu selvan 1, P.Bharat 2, S.Mathiyalagan 3 J.Anand 4 1, 2, 3, 4 PG Scholar, BIT, Sathyamangalam ABSTRACT:
More informationPart 1 of 4. MARCH
Presented by Brought to You by Part 1 of 4 MARCH 2004 www.securitysales.com A1 Part1of 4 Essentials of DIGITAL VIDEO COMPRESSION By Bob Wimmer Video Security Consultants cctvbob@aol.com AT A GLANCE Compression
More informationGeographical Routing Algorithms In Asynchronous Wireless Sensor Network
Geographical Routing Algorithms In Asynchronous Wireless Sensor Network Vaishali.S.K, N.G.Palan Electronics and telecommunication, Cummins College of engineering for women Karvenagar, Pune, India Abstract-
More informationCross Layer Protocol Design
Cross Layer Protocol Design Radio Communication III The layered world of protocols Video Compression for Mobile Communication » Image formats» Pixel representation Overview» Still image compression Introduction»
More informationMotion Tracking and Event Understanding in Video Sequences
Motion Tracking and Event Understanding in Video Sequences Isaac Cohen Elaine Kang, Jinman Kang Institute for Robotics and Intelligent Systems University of Southern California Los Angeles, CA Objectives!
More informationA Quantized Transform-Domain Motion Estimation Technique for H.264 Secondary SP-frames
A Quantized Transform-Domain Motion Estimation Technique for H.264 Secondary SP-frames Ki-Kit Lai, Yui-Lam Chan, and Wan-Chi Siu Centre for Signal Processing Department of Electronic and Information Engineering
More informationOptimized Progressive Coding of Stereo Images Using Discrete Wavelet Transform
Optimized Progressive Coding of Stereo Images Using Discrete Wavelet Transform Torsten Palfner, Alexander Mali and Erika Müller Institute of Telecommunications and Information Technology, University of
More informationModule 8: Video Coding Basics Lecture 42: Sub-band coding, Second generation coding, 3D coding. The Lecture Contains: Performance Measures
The Lecture Contains: Performance Measures file:///d /...Ganesh%20Rana)/MY%20COURSE_Ganesh%20Rana/Prof.%20Sumana%20Gupta/FINAL%20DVSP/lecture%2042/42_1.htm[12/31/2015 11:57:52 AM] 3) Subband Coding It
More informationPresented by Viraj Anagal Kaushik Mada. Presented to Dr. Mohamed Mahmoud. ECE 6900 Fall 2014 Date: 09/29/2014 1
Presented by Viraj Anagal Kaushik Mada Presented to Dr. Mohamed Mahmoud ECE 6900 Fall 2014 Date: 09/29/2014 1 Outline Motivation Overview Wireless Sensor Network Components Characteristics of Wireless
More informationFRAME-RATE UP-CONVERSION USING TRANSMITTED TRUE MOTION VECTORS
FRAME-RATE UP-CONVERSION USING TRANSMITTED TRUE MOTION VECTORS Yen-Kuang Chen 1, Anthony Vetro 2, Huifang Sun 3, and S. Y. Kung 4 Intel Corp. 1, Mitsubishi Electric ITA 2 3, and Princeton University 1
More informationHigh Efficiency Video Coding. Li Li 2016/10/18
High Efficiency Video Coding Li Li 2016/10/18 Email: lili90th@gmail.com Outline Video coding basics High Efficiency Video Coding Conclusion Digital Video A video is nothing but a number of frames Attributes
More informationSINGLE PASS DEPENDENT BIT ALLOCATION FOR SPATIAL SCALABILITY CODING OF H.264/SVC
SINGLE PASS DEPENDENT BIT ALLOCATION FOR SPATIAL SCALABILITY CODING OF H.264/SVC Randa Atta, Rehab F. Abdel-Kader, and Amera Abd-AlRahem Electrical Engineering Department, Faculty of Engineering, Port
More informationUsing animation to motivate motion
Using animation to motivate motion In computer generated animation, we take an object and mathematically render where it will be in the different frames Courtesy: Wikipedia Given the rendered frames (or
More informationLecture 8 Wireless Sensor Networks: Overview
Lecture 8 Wireless Sensor Networks: Overview Reading: Wireless Sensor Networks, in Ad Hoc Wireless Networks: Architectures and Protocols, Chapter 12, sections 12.1-12.2. I. Akyildiz, W. Su, Y. Sankarasubramaniam
More informationNew Techniques for Improved Video Coding
New Techniques for Improved Video Coding Thomas Wiegand Fraunhofer Institute for Telecommunications Heinrich Hertz Institute Berlin, Germany wiegand@hhi.de Outline Inter-frame Encoder Optimization Texture
More informationAn Imperceptible and Blind Watermarking Scheme Based on Wyner-Ziv Video Coding for Wireless Video Sensor Networks
An Imperceptible and Blind Watermarking Scheme Based on Wyner-Ziv Video Coding for Wireless Video Sensor Networks Noreen Imran a,*, Boon-Chong Seet a, A. C. M. Fong b a School of Engineering, Auckland
More informationBLIND QUALITY ASSESSMENT OF JPEG2000 COMPRESSED IMAGES USING NATURAL SCENE STATISTICS. Hamid R. Sheikh, Alan C. Bovik and Lawrence Cormack
BLIND QUALITY ASSESSMENT OF JPEG2 COMPRESSED IMAGES USING NATURAL SCENE STATISTICS Hamid R. Sheikh, Alan C. Bovik and Lawrence Cormack Laboratory for Image and Video Engineering, Department of Electrical
More informationHuman Detection. A state-of-the-art survey. Mohammad Dorgham. University of Hamburg
Human Detection A state-of-the-art survey Mohammad Dorgham University of Hamburg Presentation outline Motivation Applications Overview of approaches (categorized) Approaches details References Motivation
More information