Cascaded Multi-Resolution Image Processing for Resource-Constrained Devices
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Solution Overview
Problem
The high computational demands of machine learning models for image processing at high resolutions make them challenging to implement on resource-constrained devices, leading to inefficiencies and degraded quality in processing outputs.
Innovation Solution
A cascaded multi-resolution approach using two machine learning models, where the first model processes an input image at a lower resolution to leverage contextual information, followed by a second model processing selected subsets at a higher resolution to enhance detail, thereby reducing computational resources while maintaining high-quality image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models process images at high resolution, then image processing quality is improved, but computational resource consumption increases
Solution Approach 1:
The patent divides the image processing task into two segments: first processing a downsampled low-resolution version of the entire image to obtain a coarse result, then processing only selected high-interest regions at high resolution. This segmentation allows the system to leverage the benefits of both low-resolution (computational efficiency) and high-resolution (processing quality) approaches without suffering from their respective drawbacks.
Solution Approach 2:
The patent introduces a resolution dimension to the processing pipeline, systematically transitioning between different resolution levels. The system processes images at multiple resolution levels (low resolution for global context, high resolution for local details), adding a dimensional aspect to the processing approach that enables efficient resource utilization while maintaining quality where needed.
2Use of energy by moving object
If machine learning models process images at lower resolution, then computational resource consumption is reduced, but image processing quality degrades
Solution Approach 1:
The patent applies different processing qualities to different regions of the image based on their importance. High-resolution processing is applied selectively to high-interest regions identified by the coarse processing stage, while other regions remain at lower resolution. This local quality approach ensures computational resources are concentrated where they provide the most value, maintaining overall image processing quality while reducing total resource consumption.
Solution Approach 2:
Instead of processing the entire image at high resolution (excessive action), the system processes only the necessary portions at high resolution - specifically the high-interest regions identified by the initial low-resolution analysis. This partial action approach avoids the waste of computational resources on regions that do not require high-resolution processing, thereby reducing overall resource consumption while maintaining quality where it matters.
3Device complexity
If standard resolution machine learning models are used, then model simplicity is maintained, but resource-constrained devices cannot execute the models
Solution Approach 1:
The patent segments the computational workload into two distinct phases executed by different model resolutions. The first phase uses a simplified low-resolution model to process the entire image and identify regions of interest, while the second phase uses a high-resolution model only on selected regions. This segmentation enables resource-constrained devices to execute the processing task by breaking it into manageable steps that fit within their computational limits.
Solution Approach 2:
The patent performs preliminary processing at low resolution before committing to high-resolution processing. The low-resolution model first analyzes the entire image to identify high-interest regions, and only then does the system proceed to process those specific regions at high resolution. This preliminary action allows resource-constrained devices to avoid the full computational burden of high-resolution processing while still achieving quality results where needed.
Data Source
AI summary
Provided are systems and methods for image processing such as image modification. More particularly, example aspects of the present disclosure are directed to systems and methods for cascaded multi-resolution machine learning for performing image processing on resource-constrained devices.