Workload-Balanced Digital Image Segmentation
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Solution Overview
Problem
Existing distributed computing approaches for processing large digital images, such as medical images, often lead to uneven workload distribution among computing nodes, resulting in inefficient use of resources and increased processing time.
Innovation Solution
A method that segments digital images into portions based on the number of non-background pixels, allowing for a balanced assignment of workload across computing nodes, utilizing a workload-balancing assignment scheme that minimizes processing time and resource utilization by determining the number of non-background pixels in each segment and adjusting the workload accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If digital images are processed using distributed computing approaches, then processing capability is improved, but workload distribution among computing nodes becomes uneven
Solution Approach 1:
The digital image is divided into multiple segments, and each segment is further divided into regions based on non-background pixel density. This multi-level segmentation allows for balanced distribution of computationally intensive processing tasks across computing nodes, ensuring that nodes with higher capacity receive segments with more non-background pixels that require more processing.
Solution Approach 2:
Different regions within image segments are classified based on their non-background pixel density characteristics. Regions with high non-background pixel density are identified and assigned to computing nodes with greater processing capacity, while regions with lower density are assigned to nodes with lesser capacity. This creates a localized optimization where each computing node receives workloads matched to its capabilities.
2Power
If digital images are processed using distributed computing approaches, then processing capability is improved, but processing time increases due to uneven workload distribution
Solution Approach 1:
The image processing task is segmented into multiple regions based on non-background pixel density, allowing parallel processing across multiple computing nodes. By pre-classifying regions according to their computational requirements, the system minimizes idle time for computing nodes and ensures that all nodes can work simultaneously on appropriately sized and complexity-matched segments, thereby reducing overall processing time.
Solution Approach 2:
The system changes the parameter of workload assignment by using non-background pixel density as the basis for distribution rather than uniform segmentation. This parameter change allows the system to optimize processing time by matching computational workload to node capacity, preventing bottlenecks where some nodes wait for others to complete their tasks.
3Power
If digital images are processed using distributed computing approaches, then processing capability is improved, but resource utilization becomes inefficient
Solution Approach 1:
The system applies local quality optimization by analyzing the specific characteristics of each region within image segments, particularly the non-background pixel density. Based on this local analysis, computing nodes are assigned regions that match their resource capacity, ensuring that high-capacity nodes handle computationally intensive regions while lower-capacity nodes handle simpler regions. This prevents waste of computational resources and optimizes energy utilization across the distributed system.
Data Source
AI summary
Processing a digital image in a distributed computing environment comprising a communications network interconnecting two or more computing nodes. A segmentation of the digital image into two or more image segments is determined. For each of the image segments, a number of non-background pixels comprised by the image segment is determined. An assignment of each of the image segments to one of the computing nodes is determined. The determination of the assignment may include balancing, based on the number of non-background pixels determined for each of the image segments, the workload of the assigned computing nodes responsive to processing the image segments. Each of the assigned computing nodes may be caused to process the image segments assigned to the computing node.