As ADAS and automated driving systems grow more complex, so too does the challenge of ensuring consistent, high-quality image capture under real-world conditions. This panel explores how the IEEE P2020 image quality guidelines are being adopted—and adapted—across the automotive ecosystem. The discussion will examine how traditional lab-based metrics translate (or fail to translate) into system-level performance, and how simulation, dynamic ISP tuning, and AI-driven perception pipelines are reshaping what “good” image quality really means. Panellists will address the tension between objective image quality metrics and perception outcomes, the impact of evolving standards on validation workflows, and the growing need for alignment between testing environments, development tools, and regulatory expectations. By focusing on image quality in context—not in isolation—this session will unpack how the industry is working to close the gap between measurement and real-world safety assurance.