Software Equipment Survey

Original Flowchart

Original Flowchart

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

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

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

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

Crop Final Image Final Image

Original Flowchart

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

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

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

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

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

Refocusing (JESSE BELDEN, 2011)

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

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

Video Compression

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

Lossless Compression ErMaC - September 2002

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

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

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

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

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