Raw Image Data Processing for Photogrammetry Workflow Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for generating spatial photogrammetry data involve early conversion of raw format images to standard format, leading to sub-optimal conversion parameters, increased data size, and logistical challenges due to larger file sizes and storage requirements.
Innovation Solution
A method for processing raw format images to generate spatial photogrammetry data by creating ancillary format images using specific conversion parameters optimized for each step of the workflow, allowing for temporary storage and regeneration of standard format images as needed, reducing storage and bandwidth demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If raw format images are converted to standard format RGB images early in the workflow, then compatibility and interoperability with imaging systems and processing workflows is ensured, but image quality is lost and data integrity cannot be maintained
Solution Approach 1:
The patent performs preliminary conversion of raw format images to standard format RGB images using optimized conversion parameters before the photogrammetry processing workflow begins. This preliminary action ensures compatibility with standard imaging systems and processing workflows while maintaining image quality through careful parameter selection, thereby resolving the contradiction between adaptability and information loss.
2Manufacturing precision
If conversion parameters are optimized for one purpose (e.g., white balance or exposure), then quality for that specific post-processing task is improved, but other image qualities may deteriorate
Solution Approach 1:
The patent employs parameter changes by selecting and applying optimized conversion parameters specifically tailored for photogrammetry workflows. These parameters are carefully chosen to balance multiple image quality aspects (exposure, white balance, contrast, dynamic range) simultaneously, ensuring high conversion quality while maintaining adaptability for various post-processing tasks in spatial model generation.
3Productivity
If early batch-conversion of raw format images to standard format is performed, then processing can proceed with compatible images, but data size increases three to six times resulting in bigger image files and increased storage requirements
Solution Approach 1:
The patent applies parameter changes by converting raw format images to standard format RGB images with optimized conversion parameters that produce appropriately sized output files. This approach enables efficient photogrammetry processing with compatible image formats while controlling data size growth, thereby resolving the contradiction between productivity and quantity of data.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach maintains image quality and reduces storage and bandwidth requirements by optimizing conversion parameters for each step, avoiding permanent loss of data and minimizing image quality deterioration.
Implementation Method 1
determining spatial coordinates by triangulation based on the generated first ancillary format images
Data Source
AI summary
Geographical maps, orthomosaics, 2D/3D models and other photogrammetry products are typically generated from RGB image data. This requires a conversion step from the captured raw format image to RGB. Such conversion can result in image artifacts, blurring and reduction of image resolution, loss of dynamic range, increase in image data size etc. A method is disclosed to avoid conversion into standard RGB image formats at the time of capture and to preserve the full data integrity and image quality all the way through the photogrammetry workflow. Having the raw format image data available at all stages in the workflow will make it possible to extract exactly the data needed at any step in this workflow without compromising the data for other steps. This leads to significant savings in terms of storage space, data transfer bandwidth and processing resource requirements.