1. Software Equipment Survey

2. Original Flowchart

3. Original Flowchart

4. Synthetic Aperture Depth
Image Establishment
Overlay, Alpha Blend
Base Image Slider
Crop Final Image

5. Image Establishment Assign Reference Numbers
Stack images in an inverted pyramid 6 4 2
BASE 1 3 5
Side View

6. Overlay, Alpha Blend
Based on Establishment, Images can be overlapped appropriately
Images are then blended by lowering the opacities based on calculated regions, bottom must remain 100%
*Additional blending modes may need to be used 6 4 2 1 3 5 3 2 3 4 3 2 3

7. Base Image Slider Original Slider Base Slider *1:2 or ?:? Near Far
Think of it as, the more layers you have blended together, the better your view will be because you're maximizing the amount of angles that you're taking data from
Now the new 0 (base)
Left
Right

8. Crop Final Image
Final Image

9. Original Flowchart

10. Render
Compress the Final Image to make it smaller for ease of handling (lower bandwidth) and to save storage space
Output to external source and/or in-software viewer

11. Algorithms - Simple Explanation of BYU Open Source Code
1) Calibration
2) Point alignment
3) Refocusing

12. Calibration Input: Calibration Images
Output: Set of Auto Correlated Points

13. Point Alignment
Creates a matrix in which the calibration points are placed in a reference plane
Refocused images often perform geometric transformations like skewing, rotation, and scaling
Matrix takes into account any gaps in the calibration points

14. Refocusing Takes the image points from the image reference plane
Averages data causing the point of interest to become in sharp focus
Map-shift-average algorithm
Align all images on a reference plane
Apply a homography
Creates a translation that brings the images together or further apart

15. Refocusing
(JESSE BELDEN, 2011)

16. BYU to Original Flowchart
8 Main code files that could be of use
Calibration
Refocusing

17. BYU to Original Flowchart
Freshmen Image Sci.
Image Processing
Image Establishment
Overlay, Alpha Blend
Base Image Slider
Crop Final Image
Render
BYU
Calibration/Point Alignment
Refocusing

18. Video Compression

19. Example of what happens in a lossless compression algorithm
.tiff (tagged image file format) is a lossless compression
Algorithms decrease file size through grouping same coloured pixels together and storing that sequence of data as one instance
Really good for images with sections of the same colour
Keep details from original image

20. Lossless Compression
ErMaC - September 2002

21. Example of what happens in a lossy compression algorithm
JPEGs (Joint Photographic Experts Group) are an example of lossy compression.
Algorithm encodes each component in a colour model separately
Changes RGB to YCbCr
It uses quantization, which is irreversible
Quantization in this algorithm - compresses ranges of values into a single value, decreasing the file size
RGB - Device dependent model that blends Red, Green and Blue light into the necessary colors. YCbCR is a means of encoding RGB information

22. Example of what happens in a lossy compression algorithm for video
MPEG extended from JPEG
Compresses raw digital video and audio
Changes RGB to YCbCr
Can use Predicted-frame (inter-frame) to use the redundancy in a frame, storing only the changed data (very similar to JPEG) or
Can use I-frame (Intra-frame) that codes each frame independently

23. Recommendation Use a camera that exports the individual frames as JPEG
Try to use a mixture of both P-frame and I-frames to keep the file size at a minimum
I-frames separated by groups of pictures (GOP) (non-I-frame) to allow the user to still go back to a specific frame without worrying about that frame's dependency on other frames

24. Conclusion
BYU gave us the code that applies to image calibration, point alignment and refocusing
It showed us how Image Overlay, Alpha Blending, and the Image slider would work
Still need to find where in the code BYU outputs the data to video
Next step is to run the code to see how well it works and to understand it more

25. JPEG Compression Flowchart
Down-sampling- Reducing the rate that a signal is sampled, leads to less information/data
Forward DCT- Discrete Cosine Transformation- Simplifies the function to make processing more quick?
Quantization- Often reduces/limits the colors used in an image, decreasing file size