Camera Exposure Control via Scene-Based Machine Learning Models
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Solution Overview
Problem
Conventional automatic exposure modes in cameras use fixed brightness targets, which can result in suboptimal image quality for different types of scenes, and require time-consuming and costly code authoring and manufacturing for customizing brightness targets for various scenes.
Innovation Solution
A machine learning approach that uses training image data to generate prediction models for real-time adjustment of exposure parameters, such as exposure time and digital gain, based on scene characteristics, allowing for optimal image capture without hardcoded brightness targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed brightness target is used for automatic exposure, then real-time camera operation can proceed without complex processing, but image quality becomes suboptimal for different types of scenes
Solution Approach 1:
The system performs preliminary classification of scene types before determining exposure parameters. By pre-defining multiple brightness targets for different scene types (e.g., landscape, portrait, night scene) and classifying the current scene into one of these types, the system can quickly select the appropriate pre-prepared brightness target without performing complex real-time analysis, thus maintaining both speed and image quality.
2Manufacturing precision
If different brightness targets are hardcoded for different scene types, then optimal image capture for various scenes is achieved, but time-consuming and costly code authoring, compilation and manufacturing activities are required
Solution Approach 1:
Instead of hardcoding different brightness targets for different scene types, the system uses a machine learning model that dynamically determines the appropriate brightness target based on scene characteristics. The model takes image data as input and outputs the optimal brightness target, allowing the system to adapt to different scene types without requiring manual code authoring or manufacturing changes. This parameter-based approach replaces fixed hardcoded values with dynamic, data-driven parameter selection.
3Manufacturing precision
If machine learning is used to dynamically determine brightness targets, then optimal image capture for various scenes is achieved, but processing complexity and timing constraints increase
Solution Approach 1:
The system uses a pre-trained machine learning model that has been trained offline on large datasets of images with various scene types and their optimal brightness targets. During real-time operation, the model copies the learned knowledge from training data to make predictions about the current scene. This approach transfers complex learning capabilities to a compact model that can run efficiently on the camera device, reducing real-time processing complexity while maintaining optimization quality.
Data Source
AI summary
Methods, apparatuses and systems may provide for operating a machine learning device by obtaining training image data, conducting an offline prediction analysis of the training image data with respect to one or more real-time parameters of an image capture device, and generating one or more parameter detection models based on the offline prediction analysis. Additionally, methods, apparatuses and systems may provide for operating the image capture device by obtaining a candidate image associated with the image capture device, determining that the candidate image corresponds to a particular type of scene represented in a parameter prediction model, and adjusting one or more real-time parameters of the image capture device based at least in part on one or more parameter values associated with the particular type of scene.