ROI Motion Vector Alignment for XR Image Processing
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Solution Overview
Problem
Existing extended reality (XR) systems face challenges in efficiently aligning images to enhance XR experiences, particularly in tasks such as temporal filtering, image fusion, and resource conservation, due to the complexity of aligning entire images rather than focusing on regions of interest.
Innovation Solution
Systems and techniques are developed to determine motion vectors based on a first and second image of a scene, identify a region of interest (ROI), and align the ROI of the first image with a corresponding region of the second image using motion vectors, thereby improving alignment accuracy and reducing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If entire images are aligned using motion vectors, then alignment accuracy is improved, but computational resources and processing time increase significantly
Solution Approach 1:
The patent divides the image into multiple regions of interest (ROIs) based on motion characteristics. Instead of processing the entire image uniformly, the system identifies and processes only specific regions that contain significant motion information, thereby reducing computational load while maintaining alignment accuracy in critical areas.
Solution Approach 2:
The patent applies different processing strategies to different regions of the image. High-priority regions with significant motion are aligned with higher precision using detailed motion vectors, while low-priority regions use simplified alignment methods. This local differentiation optimizes the balance between accuracy and computational efficiency.
2Measurement precision
If motion vectors are calculated for the entire image, then temporal filtering accuracy is improved, but processing time and computational power increase
Solution Approach 1:
The patent extracts and processes only the most relevant motion information from the image by identifying regions of interest. Instead of calculating motion vectors for all pixels, the system focuses computational resources on extracting motion data from critical regions, thereby improving temporal filtering accuracy while reducing processing time.
Solution Approach 2:
The patent applies partial action by processing only the necessary portions of the image for temporal filtering. By identifying and processing only regions that contribute significantly to filtering accuracy, the system achieves adequate temporal filtering performance with reduced computational effort and faster processing.
3Manufacturing precision
If full image alignment is performed, then image fusion quality is improved, but computational resource consumption increases
Solution Approach 1:
The patent segments the image into multiple regions and performs alignment and fusion operations separately on each region. This segmentation allows the system to focus computational resources on regions that require high-quality fusion while using simplified methods for less critical regions, thereby improving overall image fusion quality while reducing total computational resource consumption.
Solution Approach 2:
The patent applies local quality by using different alignment and fusion strategies for different regions based on their importance and motion characteristics. Critical regions receive high-quality processing with detailed motion compensation, while non-critical regions use faster, less resource-intensive methods, optimizing the balance between fusion quality and computational efficiency.
Data Source
AI summary
Systems and techniques are described herein for imaging. For instance, a method for imaging is provided. The method may include determining motion vectors based on a first image of a scene and a second image of the scene; obtaining an indication of a region of interest (ROI) of the first image; identifying, based on the indication of the ROI, a set of motion vectors associated with the ROI; and aligning the ROI of the first image with a corresponding region of the second image based on the set of motion vectors to generate aligned first image data.