Machine Learning Image Adjustment Controls
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Solution Overview
Problem
Conventional image processing systems cannot customize correction algorithms to specific input images, applying generic adjustments that do not consider the content of the image being processed.
Innovation Solution
The use of machine learning to define user controls for image adjustments by analyzing new images and comparing them to a reference dataset to generate basis styles based on weighted averages of adjustment parameters, allowing for personalized adjustments such as brightness, contrast, and saturation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If generic preset adjustments are applied to images, then the processing speed is fast and the system is simple, but the adjustment quality does not consider the specific content of each image
Solution Approach 1:
The system performs preliminary analysis of the input image to identify its content characteristics (e.g., landscape, portrait, urban scene) before applying adjustments. This preliminary classification enables the selection of appropriate adjustment parameters tailored to the specific image type, thereby improving adjustment quality without requiring complex real-time processing during the actual adjustment phase
Solution Approach 2:
The system dynamically changes adjustment parameters (brightness, contrast, saturation, etc.) based on the detected image content. Different content types trigger different parameter sets, allowing the system to optimize adjustment quality for each image type while maintaining a relatively simple overall system architecture through parameter variability rather than structural complexity
2Adaptability or versatility
If content-specific analysis is performed on each image, then customized adjustments can be provided, but the processing time increases
Solution Approach 1:
The image processing system segments the adjustment process into distinct stages: content analysis phase and adjustment application phase. The content analysis is performed once to classify the image type, and then pre-defined adjustment profiles are applied based on this classification. This segmentation allows customization without requiring continuous complex analysis during the adjustment phase, thereby reducing overall processing time
Solution Approach 2:
The system creates and stores template adjustment profiles for different image content types (landscapes, portraits, urban scenes, etc.). Once the content analysis identifies the image type, the system copies and applies the corresponding pre-defined adjustment template, avoiding the need to compute custom adjustments from scratch and significantly reducing processing time while maintaining high adaptability
Data Source
AI summary
In various example embodiments, a system and method for using machine learning to define user controls for image adjustment is provided. In example embodiments, a new image to be adjusted is received. A weight is applied to reference images of a reference dataset based on a comparison of content of the new image to the reference image of the reference dataset. A plurality of basis styles is generated by applying weighted averages of adjustment parameters corresponding to the weighted reference images to the new image. Each of the plurality of basis styles comprises a version of the new image with an adjustment of at least one image control based on the weighted averages of the adjustment parameters of the reference dataset. The plurality of basis styles is provided to a user interface of a display device.