ISP Image Correction Learning for Autonomous Video Recognition

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Solution Overview

Problem

Existing image signal processing (ISP) parameter tuning for video recognition models relies heavily on manual user intervention, which is subjective and time-consuming, affecting performance and increasing production costs due to hardware requirements.

Innovation Solution

A method and device using a tuning parameter learning model to automatically generate parameters for ISP, trained on ground truth parameters derived from video recognition model outputs, to improve video analysis tasks in autonomous vehicles.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual user intervention is used for ISP parameter tuning, then the parameter can be optimized for user's naked eye recognition, but it increases time consumption and reduces productivity

Engineering Contradiction:

Improvesubjective determination accuracyVSAvoidparameter tuning speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system performs self-adjustment by automatically generating ISP tuning parameters through the learning model without requiring manual user intervention. The video recognition model itself provides the feedback signal used to tune the ISP parameters, creating a self-optimizing system that resolves the contradiction between accuracy and speed.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent implements a feedback mechanism where the video recognition model's performance on corrected video data is used to evaluate and adjust the ISP tuning parameters. This closed-loop feedback system automatically optimizes parameters based on actual recognition performance, eliminating the need for slow manual tuning while maintaining or improving accuracy.

Inventive Principle:

Principle #23Feedback

2Measurement precision

If manual user intervention is used for ISP parameter tuning, then the parameter can be optimized for user's naked eye recognition, but it affects video recognition model performance

Engineering Contradiction:

Improvesubjective determination accuracyVSAvoidvideo recognition model performance

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

Instead of tuning ISP parameters based on human visual perception (the traditional approach), the patent inverts the approach by tuning parameters based on video recognition model performance requirements. The learning model generates parameters that optimize recognition accuracy rather than subjective visual quality, directly resolving the contradiction between these two objectives.

Inventive Principle:

Principle #13The other way round (Inversion)

3Productivity

If automated algorithm is used for ISP parameter extraction, then the processing speed is improved, but it requires additional hardware components increasing production cost

Engineering Contradiction:

Improveparameter extraction speedVSAvoidhardware requirements

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent makes the video recognition model serve multiple functions: it performs both the video analysis task and provides feedback for ISP parameter tuning. This multi-functionality eliminates the need for separate dedicated hardware for parameter extraction, as the existing recognition model is repurposed to also guide ISP tuning, thereby reducing device complexity while maintaining automated speed.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent merges the ISP tuning function with the video recognition function by using the same processing system. The tuning parameter learning model is integrated with the video recognition model, combining what would traditionally be separate functions into a unified system that reduces hardware requirements and production costs.

Inventive Principle:

Principle #5Merging (Combining)

4Extent of automation

If separate chipset is added for ISP parameter extraction, then the automation is improved, but it increases production cost

Engineering Contradiction:

Improveparameter tuning automationVSAvoidproduction cost

Core Design Contradiction:

Extent of automationVSEase of manufacture

Solution Approach 1:

The system achieves full automation without additional hardware by having the video recognition model serve itself - using its own performance metrics to generate the tuning parameters it needs. This self-service capability provides complete automation while avoiding the production cost increases associated with separate chipsets.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250329169A1Method and Device for Video Analysis Based on Image Correction Learning Model

Publication Date: 2025.10.23 HYUNDAI MOTOR CO LTD

AI summary

An apparatus of a vehicle comprises a memory storing at least one instruction and a processor configured to execute the at least one instruction. The at least one instruction may be configured to cause, when executed by the processor, the apparatus to: via a tuning parameter learning model for image correction, generate, based on received video data, a tuning parameter for adjusting image signal processing (ISP) for correcting the received video data; correct, based on the tuning parameter, the received video data; identify, via a video recognition model, at least one object in at least one image corresponding to the corrected video data; and control, based on the identified at least one object, autonomous driving of the vehicle.