Post-manufacture Camera Calibration via Cloud Server
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Solution Overview
Problem
Low-end camera modules in camera-enabled devices often produce inconsistent or distorted images due to manufacturing variations in optical sensors and other optical elements, which are not adequately calibrated, leading to higher costs for testing and additional memory storage for calibration parameters.
Innovation Solution
A camera calibration system that utilizes a cloud computing server to compute sensor output adjustments based on sample photographs, applying machine learning and statistical analysis to derive calibration parameter models, which can be used to automatically adjust images and improve their quality, even surpassing those from high-end cameras by accommodating individual color preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If pre-calibration testing is performed in the lab to produce calibration parameters, then image consistency and quality are improved, but manufacturing cost and complexity increase
Solution Approach 1:
The system performs preliminary calibration actions by capturing test images during factory production and storing them in the camera module's memory. These pre-captured images serve as reference data for later calibration operations, eliminating the need for complex real-time testing during manufacturing while maintaining image consistency.
Solution Approach 2:
The patent introduces an intermediary calibration image storage mechanism between the factory testing phase and end-use calibration. The captured test images act as an intermediary data structure that bridges the gap between manufacturing specifications and runtime calibration needs, reducing both manufacturing complexity and improving image consistency.
2Manufacturing precision
If calibration parameters are stored in the camera module's memory, then image quality is improved, but memory cost and device complexity increase
Solution Approach 1:
The system applies local quality by storing calibration data only in the specific memory locations corresponding to each optical sensor's position in the matrix. Each sensor's calibration parameters are stored locally in its associated memory cell, enabling targeted calibration without requiring global memory allocation for all sensors, thus reducing overall memory requirements while maintaining image quality.
Solution Approach 2:
The patent uses copying by storing reference calibration images in the camera module's memory during manufacturing. These copied reference images are then used during runtime calibration operations, eliminating the need for large amounts of raw calibration data and reducing memory storage requirements while preserving image quality through reference-based calibration.
3Measurement precision
If factory calibration testing is performed on each camera module, then individual sensor inconsistencies are corrected, but production time and cost increase
Solution Approach 1:
The system performs preliminary calibration data capture during factory production by taking test images with each camera module. This preliminary action captures the unique characteristics of each sensor array, enabling individual sensor inconsistency correction without requiring time-consuming real-time testing during production, thus maintaining calibration accuracy while improving production speed.
Solution Approach 2:
The patent applies skipping by eliminating the need for complex real-time calibration testing during production. Instead, the system skips directly to capturing simple test images and storing reference data, then performs the actual calibration computation later using stored algorithms. This rushing through the calibration process maintains measurement precision while significantly improving productivity.
4Ease of manufacture
If low-end camera modules are produced without calibration, then device cost is reduced, but image quality and consistency deteriorate
Solution Approach 1:
The system enables self-service calibration by providing the camera module with the computational algorithms and reference data needed to perform its own calibration. Low-end modules can autonomously calibrate themselves using stored reference images and computation algorithms, eliminating the need for expensive factory calibration processes while maintaining image consistency through self-performed calibration operations.
Solution Approach 2:
The patent applies preliminary action by pre-storing calibration reference images and computation algorithms in the camera module's memory during manufacturing. This preliminary preparation enables low-end modules to perform calibration without expensive real-time testing, reducing manufacturing costs while maintaining image consistency through the use of pre-prepared calibration data and algorithms.
Data Source
AI summary
Some embodiments include a method of operating a calibration server for a camera module. The method can include: receiving, by the computing server, a first training image taken by the camera module in a mobile device and a corresponding image-context attribute from the mobile device; aggregating, by the computing device, the first training image into a set of contextually similar images based on the image-context attribute; computing a calibration parameter model based on the set of contextually similar images utilizing dimension reduction statistical analysis; and scheduling to update the calibration parameter model to configure an image processor to adjust a raw photograph of the camera module according to the calibration parameter model.