Algolux Introduces Latest Release of CRISP-ML Image Quality Tuning Automation Software


Enhancements to CRISP-ML capabilities significantly improve camera tuning effectiveness

AutoSens Conference, Brussels, Belgium – September 18, 2018 – Algolux, the leading provider of machine learning optimization platforms for autonomous vision, today announced the latest release of its award-winning CRISP-ML software platform that automates the challenging and manually intensive task of camera image quality (IQ) tuning through machine learning and objective IQ metrics (KPIs).

This release of CRISP-ML significantly improves the effectiveness of camera teams to reach their objective image quality goals through the following enhancements:

Improved tuning methodology for increased usability and flexibility to achieve customer IQ goals

  • New multi-objective “ground truth” lab charts are generated and displayed on calibrated high definition monitors to provide a more flexible lab environment than just using current physical transmissive or reflective charts (Figure 1).
  • Charts can be quickly tailored to accommodate tuning against different image quality KPI sets or even custom KPIs.
  • The Algolux KPI sets now apply an intuitive threshold approach to guide the CRISP-ML solvers to optimize the highest priority metrics.
  • Users can quickly define different tuning campaigns to best meet their customer requirements.
Figure 1 - CRISP-ML Objective Tuning Lab Setup

Enhanced image quality tuning and optimization analysis from system to component level

  • Deep analysis capabilities now allow users to traverse each parameter iteration to determine optimizer convergence and quickly filter through the tens of thousands of iterations to prioritize top results for further analysis and visual inspection of tuned images (Figures 2 and 3).
  • Sensitivity analysis enables exploration of parameter, KPI, and image signal processor (ISP) block dependencies, providing insight into their impact to image quality.
  • Stability analysis helps determine the most stable parameter setting against KPI targets to help mitigate IQ variances due to affects such as device yield and temperature.
Figure 2 - CRISP-ML ISP parameter optimization analysis and tuned chart image
Figure 3 - CRISP-ML tuned chart image and ISP tuning results scatter plot analysis (1)

Accelerated bring-up of new and archived tuning projects

  • New database repository architecture stores all tuned images, parameter settings, and KPI scores for each iteration of every tuning run for more efficient analysis and archiving.
  • Camera tuning teams have complete access for customer reporting requirements and can quickly restore prior projects to respond to customer requirement changes or to jumpstart new projects, addressing infrastructure challenges faced by teams today.

See related news “Algolux Announces NaturalIQ – Machine Learning Technology Automates Subjective Image Quality Tuning” announced today.

For more information on CRISP-ML and to access the latest release, please contact Algolux at

About Algolux

Algolux enables autonomous vision – empowering cameras to see more clearly and perceive what cannot be sensed with today’s imaging and vision systems. Computer vision is at the heart of autonomous cars, ADAS, mobile devices, security cameras (IoT), AR/VR, robotics, drones, and medical equipment, leading the next wave of market growth and social impact. Developed by an industry-recognized team of machine learning, computer vision, and image processing researchers, our patented machine-learning technologies address the complexity of optimizing imaging and vision systems while improving the costs, time-to-market, and expertise challenges faced by product development teams.

Visit us at

© Copyright 2018 Algolux, Inc. Algolux, the Algolux logo, CRISP, CRISP-ML, CANA, and other designated brands included herein are trademarks of Algolux in the United States and other countries. All other trademarks are the property of their respective owners.


Dave Tokic

+1 (877) 424-9107