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New Concepts in Image And Video Processing Utilizing Distributed Computing

New Concepts in Image And Video Processing Utilizing Distributed Computing Dr. Chance M. Glenn, Sr. Associate Professor – ECTET Department Director – The McGowan Center for Telecommunications, Innovation and Collaborative Research Rochester Institute of Technology. Content. Introduction

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New Concepts in Image And Video Processing Utilizing Distributed Computing

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  1. New Concepts in Image And Video Processing Utilizing Distributed Computing Dr. Chance M. Glenn, Sr. Associate Professor – ECTET Department Director – The McGowan Center for Telecommunications, Innovation and Collaborative Research Rochester Institute of Technology Research Computing Seminar Series

  2. Content • Introduction • Background – Chaotic Dynamics • The D-Transform Algorithm • Applications • Fourier Series Waveform Classification • Results • Cluster Usage • Acknowledgements Research Computing Seminar Series

  3. I. Introduction • Communication Technology and Networking • Demands continue to increase • Bandwidth • Throughput • Processing time (real-time applications) • Convergence • Digital Rights Management • Efficient use of bandwidth • Modulation • Compression • Network efficiency Research Computing Seminar Series

  4. II. Background – Chaotic Dynamics Vcc + L R Vc Q C Ve Re Ce + Vee • A typical chaotic oscillator is the Colpitts system. • The Colpitts circuit is a typical circuit topology used in the engineering design of oscillators. Equations of motion: Circuit diagram: where Research Computing Seminar Series

  5. II. Background – Chaotic Dynamics Research Computing Seminar Series

  6. II. Background – Chaotic Dynamics Smoothly varying waveforms Time-dependent waveforms Note the waveform variation in these segments Initial conditions Research Computing Seminar Series

  7. II. Background – Chaotic Dynamics Another chaotic system - Lorenz Equations of motion: (a) Research Computing Seminar Series

  8. II. Background – Chaotic Dynamics Suppose we have multiple chaotic oscillators, where each one may be multi-dimensional systems: s1, s2, s3, …, sM Each oscillation component can be described as: sm(n,x), where n is the component number. The z component of the Lorenz oscillation above may be described as: s2(3,x). We generated new sets of chaotic oscillations by combining these standard oscillations together to create more complex forms: cnT(x) = F[s1(1,x),…,s1(N,x),…,sM(1,x),…,sM(N,x)] wherenTis a type number (a) Research Computing Seminar Series

  9. II. Background – Chaotic Dynamics We’ve created a combined chaotic oscillation (CCO) matrix which is comprised of a set of 32 sequences, each holding 216 points of 16-bit resolution (4096 KB). 1 216 c1(i) c2(i) c3(i) c32(i) Our premise is that if we improve the metric entropy of the CCO set, then we increase the probability of matching arbitrary naturally varying waveforms (a) Research Computing Seminar Series

  10. III. The D-Transform Algorithm D D-1 d d x’ x C C k k The D-transform takes advantage of the time diversity inherent in chaotic processes. CONCEPT: segments of a digital sequence, x, such as that derived from audio, video, and image data (and other data), can be replaced by the segments extracted from a CCO matrix that matches it within an acceptable error tolerance. Symbolically, we can describe the D-transform and inverse D-transform operators as: Research Computing Seminar Series

  11. III. The D-Transform Algorithm is the original digital sequence, DYNAMAC (DY-na-mac) stands for dynamics-based algorithmic compression. is the combined chaotic oscillation matrix (static), and is the matrix ordering sequence. if where is the length function then compression occurs. We reproduce the digital sequence by The point-wise error between the original and reconstructed sequence is is the total error between the sequences. E = 0 mean lossless compression. Research Computing Seminar Series

  12. III. The D-Transform Algorithm Input Buffer Fixed Storage Initialization file Digital Sequence C Combined Chaotic Oscillation Matrix (32x65536x16) Nt x[n] Ni Ns Ns Nc Nc CO Decimator Sequence Parser BLOCK DIAGRAM (EXAMPLE) c[n] xp[n] cn[n] Ns Nc Ni Nt Scaling Comparator D-bite generator ef e[n] Ns D = [D1,D2,D3,D4] Ns DYNAMAC Implementation Research Computing Seminar Series

  13. III. The D-Transform Algorithm original image from row 400 – 64 pixels (green) Ns = 64 Research Computing Seminar Series

  14. III. The D-Transform Algorithm compression ratio c = NsNb/ND where, Ns – length of data segment Nb – bit resolution per channel ND – number of bits to represent d-bite ex. Ns = 64, Nb = 8, ND = 40 c = 12.8:1 Research Computing Seminar Series

  15. III. The D-Transform Algorithm Original BMP image Decompressed DYN image Research Computing Seminar Series

  16. IV. Applications Digital Rights Management Image scrambled with a 160-bit key Research Computing Seminar Series

  17. IV. Applications x1 x4 x2 xN x3 C ka C ka C C ku ku C ka D-1 D-1 D-1 D-1 … D-1 C ka x d D Simultaneous streaming of content to users on a network. Unauthorized users, signified with dots, will not receive quality content. Digital Rights Management Content Distribution Research Computing Seminar Series

  18. IV. Applications Biomedical Image Analysis Could be used to detect abnormalities and diseased tissue in digital images for early detection. Research Computing Seminar Series

  19. IV. Applications Video Tracking/Surveillance Subject vehicle: Black Car Error: 0.23 Research Computing Seminar Series

  20. IV. Applications Video Tracking/Surveillance Subject vehicle: Black Car 6 seconds later error: 0.12 Research Computing Seminar Series

  21. V. FSWC 1 216 c1(i) c2(i) c3(i) c32(i) Fourier Series Waveform Classification (skip 32 points) • CHALLENGE: • In order to extract oscillations from the CCO matrix, we slide a window of length Nc through each of the the 32 oscillation types. • Each extracted oscillation is compared to the original. • There will roughly be 216 extractions and comparisons • Time consuming! Research Computing Seminar Series

  22. V. FSWC Fourier Series Waveform Classification • SOLUTION: • If we can determine a method of waveform classification, we can group waveforms into families. Only similar families need be searched. • Fourier Series Decomposition: Research Computing Seminar Series

  23. V. FSWC Fourier Series Waveform Classification If we break the Fourier coefficient space into discrete partitions: We get the 18-bit sequence b = 100010110010010010 We have 3-bit resolution with 3 terms. We call this M-N FSWC (3-3 FSWC) b = bA1bB1bA2bB2bA3bB3 Generates 2MN bits Families are subsets of b Research Computing Seminar Series

  24. V. FSWC Fourier Series Waveform Classification We can pre-classify a CCO matrix and store the results. EXAMPLE: Nc = 128 extracted from three sections of c4(i) Research Computing Seminar Series

  25. V. FSWC Fourier Series Waveform Classification 10-bit family: bf = 1000111000 (568) Research Computing Seminar Series

  26. V. FSWC Fourier Series Waveform Classification 10-bit family: bf = 0100110110 (310) Research Computing Seminar Series

  27. V. FSWC Fourier Series Waveform Classification 10-bit family: bf = 0111001001 (457) Research Computing Seminar Series

  28. V. FSWC Fourier Series Waveform Classification Research Computing Seminar Series

  29. V. Results Audio Example Extracted segment 3-3 FSWC classification code: b = 111111111111100101 bf = 1111111111 (1023) 16-bit, 44100 sample/sec, single channel digital audio Research Computing Seminar Series

  30. V. Results Audio Example Family 1023 only had 230 members Speed increase: 65536/230 = 285 times Error = 0.104. The error obtained using the traditional approach is essentially the same. Research Computing Seminar Series

  31. V. Results Error calculation We found that increasing the bit resolution, M, of the FSWC procedure improved accuracy, but a reduction of the processing time improvement. The table shows processing time improvement and the error ratio for different resolutions and different family lengths. Error ratio: er = eFSWC/e0 Research Computing Seminar Series

  32. VI. Optimization CCO Matrix 1 CCO Matrix 2 Compressed image CCO waveform utilization histogram CCO Matrix 3 Research Computing Seminar Series

  33. VI. Optimization Research Computing Seminar Series

  34. VI. Optimization Research Computing Seminar Series

  35. VI. Optimization • The IBM Cluster was used to: • Make large optimization runs • Compress content for video • Do comparative studies for speed, quality, and efficiency • Develop potential hardware implementations • We learned to use it as we went. Research Computing Seminar Series

  36. VI. Conclusions and Future Work The D-transform provides a new method of analysis for digital sequences. The D-transform can be used for digital data compression, identification, digital rights management, streaming, etc. FSWC dramatically improves the process, making it more realizable as a part of other algorithms. Latency time is decreased, processing time is decreased. FSWC has implications for other fields (digital modulation, general classification). We are working to implement this complete algorithm in hardware. Utilize the cluster more efficiently Research Computing Seminar Series

  37. References • C. M. Glenn, M. Eastman, and N. Curtis, “ Digital Rights Management and Streaming of Audio, Video, and Image Data Using a New Dynamical Systems Based Compression Algorithm”, IADAT Conference on Telecommunications and Computer Networks , Conference Proceedings, September 2005. • C. M. Glenn, M. Eastman, and G. Paliwal, “A New Digital Image Compression Algorithm Based on Nonlinear Dynamical Systems”, IADAT International Conference on Multimedia, Image Processing and Computer Vision, Conference Proceedings, March 2005. • C. M. Glenn and T. Rossi, “Implementation of a New Codec for Broadcast and Digital Rights Management of High Definition Television”, 4th Annual Conference on Telecommunications and Information Technology, Conference Proceedings, March 2006. • C. M. Glenn, “Clinical Analysis of Biomedical Images Using a New Nonlinear Dynamical Systems Based Transformation”, Submitted to IEEE Transactions on Signal Processing, 2007. • Edward Ott, Chaos in Dynamical Systems, Cambridge Univ. Press, Canada, 1993.Moon book on chaos. Research Computing Seminar Series

  38. References 6. Martin J. Hasler, Electrical Circuits with Chaotic Behavior, Proceeding sof the IEEE, vol. 75, no. 8, August 1987.. 7. P. S. Lindsay, Period Doubling and Chaotic Behavior in a Driven Anharmonic Oscillator, Phys. Rev. Lett.47, 1349 (November 1981). 8. S. Hayes, C. Grebogi, E. Ott, A. Mark, Phys. Rev. Lett. 73, 1781 (1994). 9. T. Matsumoto, L. O. Chua, M. Komuro, The Double Scroll, IEEE Trans. CAS-32, no. 8 (1985), 797-818. 10. L. Nunes de Castro and F. J. Von Zuben, Recent Developments in Biologically Inspired Computing, Idea Group Publishing, 2005. 11. Richard E. Blahut, Principles and Practice of Information Theory, Addison-Wesley Publishing Company, New York, 1987. Research Computing Seminar Series

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