Dynamic Reference Region Sizing for Image Processing Resource Optimization
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Solution Overview
Problem
Existing image processing methods often result in larger area overhead and higher computational resource requirements, leading to suboptimal usage of resources and reduced performance.
Innovation Solution
An image processing method that dynamically adjusts the area overhead by deciding a first reference size of reference regions based on the computational resources and task types, optimizing the processing of input images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If lossless methods (tiling or pipeline techniques) are used to achieve better image processing performance, then image quality is improved, but area overhead and computational resource requirements increase
Solution Approach 1:
The patent applies dynamics by making the reference region size adjustable rather than fixed. The system dynamically adapts the reference region size based on available computational resources, allowing it to shrink or expand as needed. This resolves the contradiction by enabling the system to maintain image quality when resources permit while reducing area overhead when resources are constrained, eliminating the need for always using large lossless methods.
Solution Approach 2:
The patent changes the parameter of reference region size to be variable rather than constant. By adjusting this parameter based on computational resource availability, the system can optimize between image quality and area overhead. When computational resources are abundant, larger reference regions improve image quality; when resources are limited, smaller reference regions reduce area overhead while maintaining acceptable performance.
2Manufacturing precision
If lossless methods (tiling or pipeline techniques) are used to achieve better image processing performance, then image quality is improved, but computational resource requirements increase
Solution Approach 1:
The system dynamically adjusts reference region size based on available computational resources. When computational resources are plentiful, the system uses larger reference regions to achieve better image quality. When computational resources are constrained, the system reduces reference region size to lower computational requirements, thus resolving the contradiction between image quality and computational resource consumption.
Solution Approach 2:
The patent modifies the reference region size parameter according to computational resource availability. This parameter change allows the system to balance image quality against computational cost - using larger parameters when resources allow for better quality, and smaller parameters when resources are limited to reduce computational burden.
3Manufacturing precision
If fixed large reference regions are used to maintain image quality, then image quality is preserved, but area overhead increases
Solution Approach 1:
The patent makes the reference region size dynamic rather than fixed, allowing the system to adapt to different computational resource scenarios. The reference region can expand when resources are abundant to maintain image quality, and contract when resources are limited to reduce overhead, thus achieving both image quality preservation and adaptability to varying resource conditions.
Solution Approach 2:
The system changes the reference region size parameter based on computational resource availability and task types. This parameter adaptation enables the system to maintain image quality when resources permit while demonstrating versatility by adjusting to different resource constraints, thereby resolving the contradiction between quality preservation and adaptability.
4Manufacturing precision
If computational resources are increased to process images with larger reference regions, then image quality is improved, but resource usage efficiency decreases
Solution Approach 1:
The patent optimizes the reference region size parameter to match available computational resources, avoiding unnecessary resource consumption. By setting the reference region size appropriately rather than always using maximum size, the system achieves good image quality while maintaining high resource usage efficiency, thus resolving the contradiction between quality and efficiency.
Solution Approach 2:
The system dynamically optimizes resource usage by adjusting reference region size according to available computational resources and task requirements. This dynamic optimization ensures that computational resources are used efficiently - neither over-provisioning (which would reduce efficiency) nor under-provisioning (which would harm quality) - thereby achieving both image quality and resource efficiency.
Data Source
AI summary
An image processing method, applied to an image processing device, comprising: (a) deciding a first reference size of at least one reference region of an input image based on a computational resource of the image processing device or task types of tasks which are being processed by the image processing device; and (b) processing at least portion of the input image based on the reference region to generate a processed image.