Visual quality evaluation of the multi-camera visualization in automotive surround view systems

Vladimir Zlokolica, Mark Griffin, Aidan Casey, Daniela Solera, Brian Deegan, Patrick Denny, Barry Dever

Research output: Contribution to journalConference articlepeer-review

Abstract

Surround view camera systems are nowadays commonly provided/ offered by most of the car manufactures. Currently, a considerable number of different multi-camera visualization systems exist in the automotive sector, which are difficult to evaluate and compare in terms of visual performance. This is mainly due to the lack of standardized approaches for evaluation, unpredictable 3D input content, un-predictable outdoor conditions, non-standardized display units as well as visual quality requirements that are not clearly identified by the car manufactures. Recently there has been IEEE-P2020 initiative established that concerns standards for image quality for automotive systems. In this paper, we address the problem of reliably evaluating multicamera automotive surround view systems in terms of visual quality. We propose a test methodology and an efficient test system platform with a video playback system and real camera input images captured from the vehicle, which enables visual quality monitoring subjectively on the head unit display and objectively by the proposed objective quality metrics.

Original languageEnglish
JournalIS and T International Symposium on Electronic Imaging Science and Technology
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event2nd Annual Conference on Autonomous Vehicles and Machines, AVM 2018 - Burlingame, United States
Duration: 28 Jan 20181 Feb 2018

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