Neural Network Operation Count Adjustment for Image Processing Efficiency
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Solution Overview
Problem
Existing image processing technologies using neural network models require significant computational resources and time due to the uniform processing of all image data, leading to inefficiencies and resource wastage.
Innovation Solution
An image processing device and method that dynamically determine the number of operations of a neural network model based on additional data, such as image resolution, noise level, pattern complexity, and ISO sensitivity, to adaptively perform image processing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the neural network model performs the same image processing operation on all image data, then image quality is improved, but processing time and resource consumption increase
Solution Approach 1:
The patent applies dynamics by making the number of neural network operations adjustable rather than fixed. The processor dynamically determines the number of operations based on additional data characteristics (such as noise level, resolution, or scene complexity), allowing the system to adapt the processing intensity to match the actual needs of each image, thereby reducing unnecessary processing time while maintaining quality where needed
Solution Approach 2:
The patent changes the parameter of operation count from a static value to a variable determined by image characteristics. By analyzing additional data and adjusting the number of neural network operations accordingly, the system optimizes the balance between image quality and processing efficiency, avoiding uniform high-cost processing for all images
2Reliability
If the neural network model performs the same image processing operation on all image data, then image quality is improved, but resource consumption increases
Solution Approach 1:
The system dynamically adjusts computational resource allocation by varying the number of neural network operations based on image characteristics. This prevents wasteful consumption of computational resources on images that require minimal processing while ensuring sufficient resources are allocated to images needing enhanced quality
Solution Approach 2:
The patent changes the operational parameter from fixed to variable, allowing the system to optimize resource usage by matching the number of neural network operations to the actual processing needs derived from additional data analysis
3Productivity
If the number of operations is reduced, then processing time and resources are reduced, but image quality may deteriorate
Solution Approach 1:
The patent applies local quality by tailoring the number of neural network operations to the specific characteristics of each image's additional data. Different images receive different numbers of operations based on their individual needs, ensuring that quality is maintained where necessary while efficiency is improved where possible
Solution Approach 2:
The system uses feedback from analyzing additional data (such as noise levels, resolution, or scene complexity) to determine the appropriate number of operations. This feedback mechanism ensures that processing intensity is automatically adjusted to maintain quality standards while optimizing efficiency
Data Source
AI summary
An image processing device includes: a memory storing one or more instructions; and at least one processor configured to execute the one or more instructions to: receive additional data to perform an image processing operation for input image data, based on the additional data, determine a number of operations of a neural network model trained to perform the image processing operation on the input image data, and based on the determined number of the operations of the neural network model, use the neural network model to generate output image data by performing the image processing operation on the input image data.