Autonomous Camera Tuning via Multi-Modal LLM and RAG

Solution Overview

Problem

The camera tuning process is laborious, time-consuming, and resource-intensive, requiring significant manual effort from image quality engineers to address image quality issues and meet OEM preferences, which limits scalability and efficiency.

Innovation Solution

A two-stage camera tuning system utilizing a multi-modal large language model (LLM) for image quality analysis and a retrieval-augmented generation (RAG) system to produce tailored camera configuration solutions, enabling autonomous identification and correction of image quality issues in real-time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If manual camera tuning process is used, then image quality issues can be addressed, but the process becomes laborious, time-consuming, and resource-intensive

Engineering Contradiction:

Improveimage qualityVSAvoidtuning time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system enables autonomous self-tuning of camera parameters through AI models that automatically analyze images, identify quality issues, and adjust camera settings without human intervention. The autonomous camera tuning system performs the entire tuning process independently, eliminating the need for manual engineering efforts while maintaining high image quality standards.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical tuning processes with automated AI-based systems. Large language models and multi-modal models substitute human engineers' analytical and decision-making functions, automatically processing images and generating tuning configurations through intelligent algorithms rather than human judgment and manual adjustment.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Manufacturing precision

If manual camera tuning process is used, then image quality issues can be addressed, but significant manual effort from image quality engineers is required

Engineering Contradiction:

Improveimage qualityVSAvoidprocess complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The system enables autonomous self-tuning of camera parameters through AI models that automatically analyze images, identify quality issues, and adjust camera settings without human intervention. The autonomous camera tuning system performs the entire tuning process independently, eliminating the need for manual engineering efforts while maintaining high image quality standards.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical tuning processes with automated AI-based systems. Large language models and multi-modal models substitute human engineers' analytical and decision-making functions, automatically processing images and generating tuning configurations through intelligent algorithms rather than human judgment and manual adjustment.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Manufacturing precision

If manual camera tuning process is used, then camera settings can be optimized, but scalability is limited

Engineering Contradiction:

Improvecamera optimizationVSAvoidscalability

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The AI-based camera tuning system is designed to be universally applicable across different camera models, manufacturers, and image quality standards. The system can handle multiple OEM preferences and technical standards simultaneously, making it scalable from single-camera tuning to fleet-wide optimization without proportionally increasing resources.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system enables autonomous self-tuning of camera parameters through AI models that automatically analyze images, identify quality issues, and adjust camera settings without human intervention. The autonomous camera tuning system performs the entire tuning process independently, eliminating the need for manual engineering efforts while maintaining high image quality standards.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250138408A1Scalable and autonomous camera tuning system

Publication Date: 2025.05.01 INTEL CORP

AI summary

Camera tuning process is a time consuming and labor-intensive process. To address this issue, camera tuning system including a multi-modal large language model and a retrieval-augmented generation system can be implemented to intelligently and efficiently handle a camera tuning task in real-time. The multi-modal large language model can evaluate image quality and can be finetuned using high-quality labeled data and synthetically generated labeled data. The retrieval-augmented generation system can incorporate camera configuration knowledge into a vector database and can leverage a retrieved context to generate a configuration solution that addresses image quality issues identified by the multi-modal large language model. The resulting camera tuning system is a unified process that can identify image quality issues and provide configuration solutions that address both technical and aesthetic image quality concerns.