1. The development & integration of a Real-Time X-Ray image transfer system over a wireless network Final Year Project Presentation Capt David Clarke

2. Outline Of Presentation Background to Project Technology Research Project Overview Image Compression Matlab MSE Gumstix Main tasks Completed

3. Background to Project The Army Ordnance Corps recently upgraded their HOBO robot Using COFDM for video link Also use a manual x-ray system Reamda Ltd investigating use of COFDM or ethernet to send x-ray picture wirelessly Intention was to investigate the sending of a compressed image Investigate image comparison

6. Technology Research COFDM – Coded Orthogonal Frequency Division Multiplexing Non-Destructive Testing [Digital X-Ray] TIF and JPEG Gumstix and Linux

7. COFDM Multi-carrier transmission technique Uses bandwidth efficiently Very robust against channel interference Uses a Guard Interval

8. Non-Destructive Testing Involves Digital Radiography Gives Instant read-out of an x-ray image Currently it is Manually Deployed Images are quite large Must be compressed in order to transmit Non-Destructive testing is used in many industries

9. JPEG Standardised image compression algorithm Lossy image Trade-off of file size vs image quality Mathematical formula Source code freely available Many myths about JPEG

10. Project Overview Research Sample x-ray images compressed from TIF to JPEG Image Quality assessed by experts and using Matlab MSE method investigated Gumstix used to send new images

11. Image Compression Simply done using ImageConverter PLUS TIF files of 13 MB reduced to100 – 300KB when converted to JPEG On using Gumstix, it was found that zipping the files reduced size to approx 400 – 600KB 100% Quality 85% Quality

12. Image Compression Assessed Compressed Images sent to Ordnance Workshops and assessed by experts All 6 images sent were approved for use (100% Quality) 5 out of 6 images were approved for use (85% Quality) The images were scored out of ten – Mean and SD calculated JPEG Myth ?

13. Matlab Matlab used to find the MSE [Mean Square Error] of before and after images Using MSE, PSNR [Peak Signal-to-Noise Ratio] was then calculated PSNR = 10*log10((MAX(L))^2 / MSE)) Plotted against Compression rate

14. MSE Mean Square Error has many good attributes Simple to use – Natural to define energy of a signal – Possesses symmetry and differentiability - Widely used in signal processing However, using it to predict human perception of image quality is not deemed a good idea Too many assumptions – Random re-ordering - MSE wont pick up an error - Signal fidelity as important as signal error samples

15. Gumstix Connex 400xm Used to send the image to the host PC Linux had to be learned http://ohm.nuigalway.ie/wiki http://docwiki.gumstix.org Simple commands used to zip image and scp to host pc

16. Main Tasks Completed Research of Technology Knowledge gained of Army Equipment Image Compression and Comparison Matlab Understanding of Linux First time to use Gumstix

17. Thank you for your attention