Image Processing Load Balancing via Peak Pixel Distribution
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Solution Overview
Problem
Conventional image processing methods face challenges in evenly distributing computational loads across multiple resources, leading to inefficiencies in processing time due to variations in region sizes and computational loads during the region division process.
Innovation Solution
An image-processing device that generates gradient data to identify peak pixels, distributes these pixels across region label generation units based on estimated region areas, and allocates computations to ensure equalized loads by using a peak coordinates distribution unit and region area estimation, thereby minimizing differences in computational loads across units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image data is divided into regions for parallel processing, then processing speed is improved, but computational load becomes uneven across processing units
Solution Approach 1:
The patent performs preliminary actions by estimating region areas using peak pixel coordinates and luminance values before actual parallel processing. This preliminary estimation allows the system to predict computational loads and distribute work evenly across processing units, preventing load imbalance during execution.
Solution Approach 2:
The patent changes parameters by using luminance values and peak pixel coordinates to estimate region areas, then adjusting the distribution of processing tasks based on these estimated parameters. This allows dynamic adaptation of computational load distribution to match actual region sizes.
2Device complexity
If region division is performed without load estimation, then device complexity is reduced, but processing time increases due to idle resources
Solution Approach 1:
The patent replaces complex mechanical load-balancing mechanisms with a computational approach using luminance values and peak pixel coordinates. Instead of requiring complex communication and coordination between processing units, the system uses image data characteristics to pre-determine optimal task distribution.
Solution Approach 2:
The system performs self-service by using the image data's own characteristics (luminance values and peak pixel positions) to determine how to divide and distribute processing tasks. The image data itself provides the information needed for optimal partitioning, eliminating the need for external load-balancing control.
3Ease of manufacture
If peak pixels are distributed without considering region area, then computational simplicity is maintained, but load equalization fails
Solution Approach 1:
The patent performs preliminary estimation of region areas using peak pixel coordinates and luminance values before distributing processing tasks. This preliminary calculation allows the system to predict which regions will require more computational resources and distribute tasks accordingly, achieving load equalization without complex real-time monitoring.
Data Source
AI summary
An image-processing device includes: a gradient data generation unit which sequentially targets pixels, and on the basis of a luminance value, generates gradient data in which each pixel is represented using a first label of a value indicating a direction toward a pixel having a higher luminance value or a second label of a value indicating that the luminance value of the pixel is higher than all pixels located around the pixel; a plurality of region label generation units which set each pixel represented by the second label as a peak pixel, divide regions into regions including pixels belonging to the same peak pixel on the basis of peak coordinates indicating the positions of peak pixels, and generate region label data; and a peak pixel distribution unit which distributes each peak pixel to each of the region label generation units such that loads of computations are approximately equalized.