Large Scale Supercomputer Assisted and Live Video Encoding with Image Statistics
The primary purpose of this thesis is to elaborate mechanisms for encoding images as videos for live recordings and post-hoc encoding of large amounts of images on a supercomputer. A major focus was set on quality preservation and optimizing the recording setup.
Researching social insects behaviour has always been a human interest for various reasons. To examine colonies having many individuals and complex social interactions, large amounts of sample data are necessary. In the Beesbook project this sample data comes in form of images of marked honeybees. These images shall be evaluated automatically using custom software, which requires excellent image quality and resolution. This master thesis introduces various improvements to the acquisition process to obtain images of optimal quality at minimal cost. Also large amounts of existing data are required to be compressed, aided by supercomputer. For the live recordings a custom IR lighting system was introduced, recording software using a GPU encoder was developed and image statistics for calibration and surveillance have been introduced. Also software was created to automatically to large scale compression of videos.
Evaluation has shown that the newly introduced lighting system not only comes at 23.7% of the price of a conventional lighting system, but also has good illumination properties. Different mechanisms are provided to analyse images and configure recording setup for optimal quality. Existing data could be compressed using the HEVC video coded from 289 TB to 80.6 TB, saving 72.1% of space with negligible loss in image quality. Finally recording software was developed to achieve this level of compressionn during live recording.