An experimental evaluation of image input degradation on machine learning performance
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Typ
Examensarbete för masterexamen
Program
Computer systems and networks (MPCSN), MSc
Publicerad
2020
Författare
Andersson, Oscar
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Significant advancements in the field of machine learning have been made during
the past decade due to artificial neural networks being feasible to compute. To train
these neural networks, a large amount of data is needed, especially in the field of
autonomous driving where image data needs to be stored at a large scale. Such data
can be reduced in size significantly by using lossy video compression at the cost of
losing visual fidelity. This trade-off could potentially be balanced such that the data
size of a dataset is reduced while the reduction in data quality is not significantly
affected. This thesis aims to establish the effects of video compression on machine
learning algorithms performing computer vision tasks, specifically for autonomous
driving. This involved evaluating the effect of certain encoders and coding parameters on a set of ML-algorithms. Multi-objective optimisation was performed to
find sets of optimum coding parameters for each encoder evaluated, referred to as
coding parameter sweet spots. Experiments were conducted to measure the impacts
of video compression, using the optimum coding parameters’, on machine learning
algorithms. The experiment results indicated that some encoders’ sweet spots were
able to compress the data without significantly altering ML-performance. Collected
experiment data was also used to compare encoders capabilities and behaviour when
compressing data for ML usage. Finally, suggestions for how practitioners should
evaluate and validate lossy video compression methods were provided.
Beskrivning
Ämne/nyckelord
Machine learning , video compression , optimisation , codec , encoder , parameters , autonomous driving