Distributed Image Scaling With Parallel Boundary Alignment
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Solution Overview
Problem
Image scaling processes that work on an entire region of interest at once or in a serial manner are computationally expensive and slow, especially for high-resolution images, leading to underutilization of hardware resources and limiting scalability and throughput in systems with multiple scaler cores.
Innovation Solution
Distributed image scaling involves dividing the region of interest into sub-regions and scaling them in parallel using multiple scaler cores, with precise alignment information to ensure artifact-free boundaries, allowing parallel processing across hardware resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image scaling is performed on the entire region of interest at once in a serial manner, then image quality and alignment precision are maintained, but processing speed decreases and hardware resource utilization is underutilized
Solution Approach 1:
The patent divides the region of interest into multiple sub-regions that can be processed independently and in parallel by different scaler cores. Each sub-region is scaled separately with calculated alignment information to ensure seamless reconstruction of the final scaled image, thereby enabling parallel processing while maintaining image quality.
2Productivity
If distributed parallel scaling is implemented to improve processing speed, then productivity increases, but alignment precision may deteriorate due to boundary artifacts between sub-regions
Solution Approach 1:
The patent performs preliminary calculations of alignment information (such as offset values and transformation parameters) before distributing the scaling task to multiple scaler cores. This pre-computed alignment data is embedded in the workload instructions, enabling each core to independently scale its sub-region while maintaining precise alignment with adjacent sub-regions, thus avoiding boundary artifacts.
3Device complexity
If single-pass scaling is used to maintain simplicity, then device complexity is low, but scalability is limited and throughput cannot be improved with multiple scaler cores
Solution Approach 1:
The patent segments the image scaling task into independent sub-tasks that can be distributed across multiple scaler cores. The system maintains a relatively simple architecture by using a workload generator to create and distribute these segmented tasks, allowing scalability to improve throughput without significantly increasing device complexity.
4Adaptability or versatility
If the scaling factor is non-integer and not known a priori, then adaptability to different scaling requirements is improved, but calculating precise alignment information becomes more complex
Solution Approach 1:
The patent dynamically calculates alignment parameters (such as offset values and sampling positions) based on the actual scaling factor, which can be any non-integer value determined at runtime. This parameter-based approach allows the system to adapt to different scaling requirements while managing calculation complexity through systematic formulas for determining alignment information.
Data Source
AI summary
Image scaling processes that work on an entire region of interest at once or in a serial manner can be computationally expensive and slow, especially for high-resolution images. To address this issue, distributed image scaling can be performed to improve real-time performance. Distributed image scaling involves dividing a region of interest of an image into sub-regions and scaling them in parallel using multiple scaler cores. The workloads to the parallel scaler cores include precise alignment information to avoid artifacts at sub-region boundaries in the scaled image.