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The data conundrum: compression of automotive imaging data and deep neural network based perception
  DOI :  10.2352/issn.2694-118X.2021.LIM-78  Published OnlineSeptember 2021
Abstract

Video compression in automated vehicles and advanced driving assistance systems is of utmost importance to deal with the challenge of transmitting and processing the vast amount of video data generated per second by the sensor suite which is needed to support robust situational awareness. The objective of this paper is to demonstrate that video compression can be optimised based on the perception system that will utilise the data. We have considered the deployment of deep neural networks to implement object (i.e. vehicle) detection based on compressed video camera data extracted from the KITTI MoSeg dataset. Preliminary results indicate that re-training the neural network with M-JPEG compressed videos can improve the detection performance with compressed and uncompressed transmitted data, improving recalls and precision by up to 4% with respect to re-training with uncompressed data.

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Pak Hung Chan, Georgina Souvalioti, Anthony Huggett, Graham Kirsch, Valentina Donzella, "The data conundrum: compression of automotive imaging data and deep neural network based perceptionin Proc. IS&T London Imaging Meeting 2021: Imaging for Deep Learning,  2021,  pp 78 - 82,  https://doi.org/10.2352/issn.2694-118X.2021.LIM-78

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