Region-Based Brightness Control with Segmented Image Exposure
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Solution Overview
Problem
Existing image capture devices lack the ability for users to adjust local brightness levels for specific regions of an image independently of the overall brightness, limiting the flexibility in capturing and enhancing images with varying light conditions.
Innovation Solution
A user interface and exposure compensation technique that allows users to adjust local brightness levels for a target region, such as a portrait, separately from the global brightness of the image, with real-time preview and application of adjustments before capturing the image, using machine learning-based segmentation and bilateral grid algorithms to mitigate halo artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If global brightness adjustment is applied to the entire image, then the overall illumination is improved, but the ability to control local region brightness independently is lost
Solution Approach 1:
The image is segmented into multiple regions (e.g., face region, body region, background regions) using machine learning-based segmentation algorithms. Each region can then have independent brightness control applied to it, allowing users to adjust local brightness for specific areas while maintaining global brightness for the rest of the image. This resolves the contradiction by enabling both global and local brightness control simultaneously.
Solution Approach 2:
Different brightness adjustment parameters are applied to different regions of the image. The system allows users to set distinct brightness levels for different segments (e.g., brighter face region, darker background), thereby achieving local quality customization. This enables independent control of local region brightness while maintaining overall image brightness balance.
2Adaptability or versatility
If local brightness adjustment is applied to specific regions, then regional brightness control flexibility is improved, but halo artifacts are introduced
Solution Approach 1:
The system uses machine learning-based segmentation as an intermediary layer between the user's brightness adjustment input and the final image output. The segmentation model intelligently identifies region boundaries and applies brightness adjustments while considering contextual information, which helps prevent harsh transitions and halo artifacts at region boundaries. The intermediary segmentation process ensures smooth blending between adjusted and unadjusted areas.
3Ease of operation
If real-time brightness adjustment is provided, then user interaction responsiveness is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary machine learning-based segmentation of the image into distinct regions before applying brightness adjustments. By pre-segmenting the image and identifying key regions (faces, bodies, backgrounds), the system reduces the computational complexity of real-time brightness adjustment. The segmentation is done once, and then brightness parameters can be applied efficiently to each pre-identified region, enabling real-time preview without excessive computational burden.
Data Source
AI summary
An example method includes displaying, by a graphical user interface of a computing device, an image comprising a target region. The target region may be smaller than an entirety of the image. The method includes providing, by the graphical user interface, a user-adjustable control to adjust a desired local brightness exposure level for the target region. The method includes receiving, by the user-adjustable control, a user indication of the desired local brightness exposure level for the target region. The method includes adjusting the local brightness exposure level for the target region in the image in response to the user indication.