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Compressive Sensing for Multimedia Communications in Wireless Sensor Networks

Compressive Sensing for Multimedia Communications in Wireless Sensor Networks. EE381K-14 MDDSP Literary Survey Presentation March 4 th , 2008. By: Wael Barakat Rabih Saliba. Recall Compressive Sensing (CS). CS combines acquisition & compression . Measurement, Reconstruction.

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Compressive Sensing for Multimedia Communications in Wireless Sensor Networks

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  1. Compressive Sensing for Multimedia Communications in Wireless Sensor Networks EE381K-14 MDDSPLiterary Survey Presentation March 4th, 2008 By:Wael Barakat Rabih Saliba

  2. Recall Compressive Sensing (CS) • CS combines acquisition & compression. • Measurement, • Reconstruction. • Objective: examine the benefits of CS when used in wireless sensor networks for imaging purposes.

  3. M << N N N M M Reconstruction Project Q[.] Framework • 3 Test Images: • Grayscale, • Quality measure: Structural Similarity Index (SSIM)

  4. Need for Quantization • Measurement vector is real-valued • Quantize measurements for digital transmission • 2 float implementations: • [8 6] quantization, • [16 9] quantization. [ word_length exponent_length ] (in bits)

  5. Peppers – [16 9] Quantization 5,000 Measurements(7.6%) 13,232 Measurements(20.2%) 21,866 Measurements(33.4%) Original

  6. Peppers – [8 6] Quantization 5,000 Measurements(7.6%) 13,232 Measurements(20.2%) 21,866 Measurements(33.4%) Original

  7. Barbara – [8 6] Quantization 5,000 Measurements(7.6%) 13,232 Measurements(20.2%) 21,866 Measurements(33.4%) Original

  8. Lena – [8 6] Quantization 5,000 Measurements(7.6%) 13,232 Measurements(20.2%) 21,866 Measurements(33.4%) Original

  9. SSIM - Lena

  10. SSIM Comparison

  11. Numerically… • Image size by format: • TIFF: 64 KB • JPEG: 45.6 KB (maximum compression) • 30% Measurements: 19.2 KB (with [8 6] quantization) • Reduction by 58%! (from JPEG) => in terms of transmitted bits, and => energy consumption at sensor

  12. References I • E. Candès, “Compressive Sampling,” Proc. International Congress of Mathematics, Madrid, Spain, Aug. 2006, pp. 1433-1452. • M. Duarte, M. Wakin, D. Baron, and R. Buraniak, “Universal Distributed Sensing via Random Projections”, Proc. Int. Conference on Information Processing in Sensor Network, Nashville, Tennessee, April 2006, pp. 177-185. • R. Baraniuk, J. Romberg, and M. Wakin, “Tutorial on Compressive Sensing”, 2008 Information Theory and Applications Workshop, San Diego, California, February 2008. • M. Wakin, J. Laska, M. Duarte, D. Baron, S. Sarvotham, D. Takhar, K. Kelly and R. Baraniuk, “An Architecture for Compressive Imaging”, Proc. Int. Conference on Image Processing, Atlanta, Georgia, October 2006, pp. 1273-1276.

  13. References II • Baraniuk, R.G., "Compressive Sensing [Lecture Notes]," IEEE Signal Processing Magazine, vol. 24, no. 4, pp. 118-121, July 2007. • M. Duarte, M. Davenport, D. Takhar, J. Laska, T. Sun, K. Kelly and R. Baraniuk, “Single-Pixel Imaging via Compressive Sampling”, IEEE Signal Processing Magazine [To appear]. • Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004. • SSIM Code: http://www.ece.uwaterloo.ca/~z70wang/research/ssim/ • L1-Magic Code & Documentation: http://www.acm.caltech.edu/l1magic/

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