1. EE381K-14 MDDSP Literary Survey Presentation March 4th, 2008
Compressive Sensing for Multimedia Communications in Wireless Sensor Networks
EE381K-14 MDDSP Literary 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. Framework 3 Test Images: Grayscale,
Quality measure: Structural Similarity Index (SSIM)
M << N
Project
Reconstruction N M
Q[.]

4. [ word_length exponent_length ] (in bits)
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%)
Original

9. SSIM - Lena

10. SSIM Comparison

11. Numerically… Image size by format: Reduction by 58%! (from JPEG)
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
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
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

13. References II
Baraniuk, R.G., "Compressive Sensing [Lecture Notes]," IEEE Signal Processing Magazine, vol. 24, no. 4, pp , 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 , Apr
SSIM Code:
L1-Magic Code & Documentation: