Surround View Stitching Quality Assessment for Seamless Composite Images
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Solution Overview
Problem
Existing image stitching technologies struggle to create seamless and realistic composite representations from multiple camera views in complex environments, often resulting in visible seams, ghosting, and other artifacts that do not align with subjective viewer quality assessments.
Innovation Solution
A system that utilizes a stitching quality assessment metric to adjust stitching parameters using a trained classifier to infer subjective quality, blending images based on objective quality metrics and scene content, optimizing the stitching process for improved visual coherence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional image stitching algorithms are used to combine multiple camera views, then the stitching process can be completed, but visible seams and image artifacts appear in the composite representation
Solution Approach 1:
The system employs a feedback mechanism where a trained classifier evaluates the stitching quality of composite images and provides quality scores. This feedback loop allows the system to iteratively adjust stitching parameters (such as blending weights, seam locations, and transformation matrices) to minimize visible seams and artifacts while maximizing overall stitching quality according to human preferences.
Solution Approach 2:
The invention dynamically changes stitching parameters based on scene content and quality assessment results. Different blending modes, seam positions, and geometric transformation parameters are adjusted according to the specific characteristics of each image pair and the evaluated quality metrics, enabling adaptive optimization rather than using fixed parameters.
2Object-generated harmful factors
If stitching parameters are adjusted to reduce seams, then seam visibility decreases, but processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-training a classifier model on a dataset of stitched images with known quality annotations before actual stitching operations. This pre-trained classifier can quickly evaluate stitching quality without requiring extensive computational resources during the actual stitching process, enabling fast parameter optimization.
Solution Approach 2:
The stitching system uses its own output (the composite image) to evaluate its quality through the trained classifier, which then automatically adjusts parameters to improve results. This self-service mechanism eliminates the need for manual intervention or external quality assessment, streamlining the optimization process.
3Manufacturing precision
If complex stitching algorithms are used to handle random scenes, then stitching completeness improves, but realism and visual coherence deteriorate
Solution Approach 1:
The trained classifier provides feedback on the realism and visual coherence of stitched images by comparing them against human preference patterns learned during training. This feedback enables the system to identify and correct unrealistic artifacts while maintaining complete scene coverage, even in complex random scenes.
Solution Approach 2:
The system dynamically adjusts stitching parameters based on scene complexity and content type. For random or complex scenes, the classifier guides parameter adjustments to preserve scene completeness while enhancing visual realism through adaptive blending and seam placement strategies specific to each scene type.
Data Source
AI summary
Stitching of multiple images into a composite representation can be performed using a set of stitching parameters determined based, at least in part, upon a subjective stitching quality assessment value. A stitched image can be compared against its constituent images to obtain one or more objective quality metrics. These objective quality metrics can be fed, as input, to a trained classifier, which can infer a subjective quality assessment metric for the stitched (or otherwise composited) image. This subjective quality assessment metric can be used to adjust one or more compositing parameter values in order to provide at least a minimum subjective quality assessment value for composited images.