1 / 13

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.

jenski
Télécharger la présentation

Compressive Sensing for Multimedia Communications in Wireless Sensor Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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/

More Related