Friday, September 27, 2019

ISP Optimization for Automotive Applications

MDPI publishes Valeo paper "Overview and Empirical Analysis of ISP Parameter Tuning for Visual Perception in Autonomous Driving" by Lucie Yahiaoui, Jonathan Horgan, Brian Deegan, Senthil Yogamani, CiarĂ¡n Hughes, and Patrick Denny.

"Image quality is a well understood concept for human viewing applications, particularly in the multimedia space, but increasingly in an automotive context as well. The rise in prominence of autonomous driving and computer vision brings to the fore research in the area of the impact of image quality in camera perception for tasks such as recognition, localization and reconstruction. While the definition of “image quality” for computer vision may be ill-defined, what is clear is that the configuration of the image signal processing pipeline is the key factor in controlling the image quality for computer vision. This paper is partly review and partly positional with demonstration of several preliminary results promising for future research. As such, we give an overview of what is an Image Signal Processor (ISP) pipeline, describe some typical automotive computer vision problems, and give a brief introduction to the impact of image signal processing parameters on the performance of computer vision, via some empirical results. This paper provides a discussion on the merits of automatically tuning the ISP parameters using computer vision performance indicators as a cost metric, and thus bypassing the need to explicitly define what “image quality” means for computer vision."

Alexis Lluis Gomez, Senior Manager of Image Quality, ARM, talks about automotive ISP optimization too in his Autosens Detroit presentation:

Manjunath Somayaji, Director of Imaging R&D, GEO Semiconductor, presents its view on ISP optimization:

On its side, Algolux says that ISP should be tightly integrated into the whole CV pipeline, rather than optimized as a separate block:

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