Content-Adaptive Image Stitching for Parallax Error Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image stitching technologies face challenges in efficiently combining images from multiple cameras, particularly in wide-view setups, due to parallax errors and motion artifacts, which can result in reduced image quality and increased computational complexity.
Innovation Solution
A content-adaptive image stitching method that selects between static seam-based, dynamic seam-based, and dynamic warp-based stitching schemes based on motion and disparity measures, adaptively transforming overlapping regions from fisheye images to rectangular images to reduce computational complexity and improve image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex stitching algorithms are used to improve image quality, then image quality is improved, but processing complexity increases
Solution Approach 1:
The patent applies parameter changes by dynamically selecting different stitching algorithms based on motion magnitude and disparity measures. When motion is small and disparity is low, a simple averaging algorithm is used. When motion is large or disparity is high, more complex algorithms like gradient-based or feature-based stitching are selected. This adaptive parameter selection resolves the contradiction by matching algorithm complexity to actual image content requirements.
Solution Approach 2:
The patent implements local quality by applying different stitching algorithms to different regions of the image based on local motion and disparity characteristics. The image is divided into multiple blocks, and each block is processed with an algorithm appropriate to its local content. This resolves the contradiction by applying complexity only where needed rather than uniformly across the entire image.
2Manufacturing precision
If advanced stitching algorithms are used to reduce parallax errors, then image quality is improved, but computational resources increase
Solution Approach 1:
The system dynamically changes processing parameters by selecting from multiple algorithm complexity levels based on measured motion and disparity. This resolves the contradiction by using computational resources proportionally to the actual difficulty of the stitching task, rather than always applying the most computationally intensive algorithm.
Solution Approach 2:
The patent applies partial action by using simple averaging algorithms for regions with low motion and disparity, reserving complex algorithms only for regions where they are truly needed. This partial application of complex algorithms reduces overall computational resource consumption while maintaining image quality where it matters most.
3Measurement precision
If content-adaptive algorithm selection is implemented, then stitching accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating motion measures and disparity measures for each image block before selecting the stitching algorithm. These preliminary measurements enable rapid algorithm selection without requiring complex real-time analysis during the stitching process itself, thus improving accuracy while minimizing additional processing time.
Solution Approach 2:
The patent segments the image into multiple blocks and processes each block independently with algorithm selection based on local characteristics. This segmentation allows parallel processing of different blocks, reducing overall processing time while maintaining high stitching accuracy through localized algorithm optimization.
Data Source
AI summary
A method for stitching images by an electronic device is described. The method includes obtaining at least two images. The method also includes selecting a stitching scheme from a set of stitching schemes based on one or more content measures of the at least two images. The set of stitching schemes includes a first stitching scheme, a second stitching scheme, and a third stitching scheme. The method further includes stitching the at least two images based on a selected stitching scheme.