Entrance Pupil Distortion Parameter Refinement in HEIF
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Solution Overview
Problem
Current camera calibration methods fail to accurately account for entrance pupil distortion, leading to inaccuracies in image projection and transformation, especially in applications requiring end-to-end distortion removal.
Innovation Solution
The proposed solution involves determining camera and distortion parameters, including entrance pupil parameters, and refining these based on control points to correct for distortions, allowing for seamless integration into the High Efficiency Image File Format (HEIF) for improved image formation and manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current camera calibration methods are used, then the calibration process is simple, but the accuracy of image projection and transformation deteriorates due to failure to account for entrance pupil distortion
Solution Approach 1:
The calibration method is segmented into distinct stages: initial calibration to obtain basic camera parameters, followed by a refinement stage that specifically addresses entrance pupil distortion. This segmentation allows the complex task to be broken down into manageable steps, improving accuracy without overwhelming complexity.
Solution Approach 2:
The method performs preliminary calibration to obtain initial camera parameters before refining the distortion parameters. This preliminary action establishes a baseline that guides the subsequent refinement process, ensuring that the complex refinement is focused and efficient rather than starting from scratch.
2Manufacturing precision
If entrance pupil distortion is not accounted for, then the calibration process remains simple, but the accuracy of distortion removal deteriorates in applications requiring end-to-end distortion correction
Solution Approach 1:
The refinement process acts as an intermediary between initial calibration and final distortion removal. It introduces controlled complexity by specifically modeling entrance pupil distortion parameters, which serve as a bridge to achieve accurate end-to-end distortion correction without requiring complete redesign of the calibration system.
Solution Approach 2:
The method changes specific distortion parameters (entrance pupil parameters) while keeping other camera parameters fixed from the initial calibration. This selective parameter change approach improves distortion removal accuracy by focusing computational effort on the most critical distortion sources without unnecessarily complicating the entire parameter set.
3Measurement precision
If traditional calibration methods are used, then computational resources are conserved, but the mapping accuracy between 3D coordinates and 2D image projections deteriorates
Solution Approach 1:
The refinement process applies partial action by focusing computational resources only on optimizing entrance pupil distortion parameters rather than recalculating all camera parameters. This selective refinement achieves improved mapping accuracy while consuming fewer computational resources than a complete recalibration would require.
Solution Approach 2:
The method substitutes a simplified refinement model for the complete calibration mechanism. Instead of running the full calibration process again, it uses a targeted optimization approach that replaces the mechanical repetition of complete calibration with a more efficient mathematical refinement process, maintaining accuracy while improving computational efficiency.
Data Source
AI summary
An apparatus may be configured to: determine at least one image; determine at least one camera parameter; determine at least one distortion parameter, wherein the at least one distortion parameter comprises at least one entrance pupil parameter; determine at least one control point of the at least one image; and refine the at least one distortion parameter based, at least partially, on the at least one control point and the at least one camera parameter.