360 VR Image Stitching Workflow Allocation
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Solution Overview
Problem
Current 360-degree virtual reality (VR) broadcast services face challenges in providing high-quality image stitching due to the requirement of many computing resources and difficulties in processing high-definition 360-degree VR images efficiently, especially when user motion or selection changes occur.
Innovation Solution
A method and apparatus for allocating tasks required for image stitching to multiple media processing entities, utilizing various parameters such as media, camera, projection, and cloud parameters to determine the necessary number of processing entities and allocate tasks effectively, enabling efficient image stitching and processing in a cloud platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image stitching is performed using traditional single-node processing, then processing simplicity is maintained, but processing speed and computational efficiency deteriorate
Solution Approach 1:
The patent divides the image stitching workflow into multiple independent tasks (decoding, feature extraction, camera parameter extraction, projection, seam information extraction, blending, encoding) that can be executed in parallel across multiple media processing entities. This segmentation enables simultaneous processing of different computational stages, significantly improving processing speed while maintaining manageable complexity through standardized task interfaces.
Solution Approach 2:
The patent transitions from single-node sequential processing to multi-node parallel processing by introducing a cloud platform architecture. This dimensional change from one-dimensional sequential execution to multi-dimensional parallel execution enables concurrent task processing and dramatically improves productivity for computationally intensive image stitching operations.
2Manufacturing precision
If high-definition 360-degree VR images are processed, then image quality is improved, but computing resource requirements and processing time worsen
Solution Approach 1:
The patent segments the computationally intensive image stitching process into separate tasks that can be distributed across multiple media processing entities. Each entity handles specific computational segments (e.g., feature extraction, projection, blending), enabling parallel processing that reduces total computing resource consumption while maintaining high-definition image quality through preserved processing precision at each stage.
Solution Approach 2:
The patent utilizes various parameters (media parameters, camera parameters, projection parameters, stitching parameters, cloud parameters) to optimize the processing workflow. By dynamically adjusting these parameters across different processing entities and tasks, the system achieves efficient resource allocation and processing that maintains high image quality while reducing overall computational resource consumption.
3Productivity
If image stitching tasks are distributed to multiple media processing entities, then processing efficiency is improved, but task allocation complexity worsens
Solution Approach 1:
The patent implements a universal task manager that handles multiple functions including task allocation, progress monitoring, and coordination across all media processing entities. This multi-functional approach consolidates complexity management into a single centralized system, improving workflow efficiency while preventing task allocation complexity from scaling linearly with the number of processing entities.
Solution Approach 2:
The patent incorporates feedback mechanisms where the task manager monitors task progress from each media processing entity and uses this information to dynamically adjust task allocation and resource distribution. This feedback loop enables efficient adaptive task management that improves productivity while keeping allocation complexity manageable through automated adjustments based on real-time status information.
Data Source
AI summary
Disclosed herein is a method of creating an image stitching workflow including acquiring 360-degree virtual reality (VR) image parameters necessary to makes a request for image stitching and create the image stitching workflow, acquiring a list of functions applicable to the image stitching workflow, creating the image stitching workflow based on functions selected from the list of functions, determining the number of media processing entities necessary to perform tasks configuring the image stitching workflow and generating a plurality of media processing entities according to the determined number of media processing entities, and allocating the tasks configuring the image stitching workflow to the plurality of media processing entities.