Autotuning Framework for Image Signal Processor Parameter Optimization
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Solution Overview
Problem
Manual tuning of image signal processing (ISP) modules in autonomous driving vehicles is labor-intensive and inefficient, making it difficult to find balanced parameter values for optimal image quality.
Innovation Solution
A closed-loop autotuning framework that uses optimization algorithms to generate and test different sets of parameter values, comparing processed images to a reference image to determine optimal values, which are then stored in a cloud database for selection based on environment, allowing for automatic configuration of the ISP module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual tuning of ISP module parameters is performed, then parameter values can be adjusted, but the process becomes labor-intensive and inefficient
Solution Approach 1:
The system performs automatic tuning without human intervention by implementing a closed-loop framework where the ISP module tunes its own parameters based on image quality assessment. The processor automatically generates candidate parameter values, processes test images, evaluates quality metrics, and selects optimal parameters, making the system self-sufficient in the tuning process.
Solution Approach 2:
The patent replaces the mechanical manual adjustment process with an automated computational system. Instead of manually adjusting parameters through interface controls, the system uses a processor to automatically generate, test, and optimize parameter values through algorithmic evaluation of image quality metrics, substituting human operation with automated computing.
2Measurement precision
If manual tuning of ISP module parameters is performed, then parameters can be adjusted one at a time, but it becomes hard to find balanced parameter values
Solution Approach 1:
The tuning process is segmented into distinct automated stages: generating candidate parameter values, processing test images with each set of parameters, evaluating image quality using objective metrics, and selecting optimal parameters. This segmentation allows systematic exploration of parameter space while maintaining manageable complexity through structured evaluation.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where image quality metrics are continuously evaluated based on processed images, and this feedback drives the selection of optimal parameter values. The processor uses the quality assessment results to determine which parameter values to adopt, creating a self-correcting optimization loop that achieves balanced parameter tuning.
3Productivity
If automatic tuning framework is implemented, then labor required for tuning is reduced, but system complexity increases
Solution Approach 1:
The processor performs multiple functions within a single integrated system: it generates candidate parameter values, processes images through the ISP module, evaluates image quality using various metrics, and selects optimal parameters. This multi-functionality consolidates what could be separate complex subsystems into a unified processing unit, achieving automation without proportionally increasing overall system complexity.
Solution Approach 2:
The patent introduces an intermediary image quality assessment mechanism that mediates between parameter selection and final image output. This intermediary evaluation layer provides objective feedback on parameter performance, enabling automated optimization without requiring complex direct control mechanisms, thus balancing automation benefits with system complexity.
Data Source
AI summary
The disclosure describes an autotuning framework for tuning an image signal processing (ISP) module of an autonomous driving vehicle (ADV). The autotuning framework can generate different sets of parameter values using a variety of optimization algorithms to configure the ISP module. Each processed image generated by the ISP module configured with the different sets of parameter values is compared by an ISP module testing device with a reference image stored therein to generate an objective core measuring one or more differences between each of the processed images and the reference image. The objective scores and the corresponding sets of parameter values are stored in a database. Different sets of optimal parameter values for different environments can be selected from the database, and uploaded to a cloud database for use by an ADV, which can select a different set of ISP parameter values based on an environment that the ADV is travelling in.