Improving the Computational Speed of a Tensor-Networked Kalman Filter for Streaming Video Completion

Improving the Computational Speed of a Tensor-Networked Kalman Filter for Streaming Video Completion

van Koppen, Bram (TU Delft Mechanical, Maritime and Materials Engineering)

Batselier, K. (mentor)
Menzen, C.M. (graduation committee)

Delft University of Technology

Mechanical Engineering | Systems and Control

2022-07-27

Streaming video completion is the practice that aims to fill in missing or corrupted pixels in a video stream by using past uncorrupted data. A method to tackle this problem is recently introduced called a Tensor Networked Kalman Filter (TNKF). It shows promising results in terms of performance compared to state-of-the-art methods for high percentages of missing pixels (≥ 95%). The main drawback of using a TNKF is the computational speed, which needs to be improved to compete with other existing methods and to be carried out in real-time by a regular computer. This work discusses three methods that reduce the computational load of the algorithm, which speeds up computations. The first method is replacing the existing algorithm with a Block Update TNKF. Secondly, the use of randomized rounding instead of deterministic rounding is investigated. The last method is the simplification of the TNKF update. Results that are presented in this report show that significant speedups of up to +132% can be achieved. In most situations, the considered speedup methods compromise the reconstruction’s accuracy. This thesis discusses the effects this has on the quality of the reconstruction.

tensor networks
Kalman Filter
video streaming
algorithm
Randomization

http://resolver.tudelft.nl/uuid:17cf3d30-23ff-4943-802c-897582e0c426

The code used in this thesis can be found in the following Git repository: https://gitlab.com/bramvankoppen/thesis

Student theses

master thesis

© 2022 Bram van Koppen