Image Processing Apparatus for Multi-Light Source Color Balance
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Solution Overview
Problem
Conventional image processing methods that apply white balance to the entire capturing screen fail to achieve proper color balance when different areas are irradiated with light from various sources, leading to color shifts.
Innovation Solution
An image processing apparatus and method that divide an input image into categories based on scene recognition, white balance detection, object recognition, and face detection, allowing for the application of different image processing parameters to each area and subsequent combination of processed areas to form a balanced image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If white balance is applied to the entire capturing screen, then the processing is simple and fast, but color shifts occur when different areas are irradiated with light from various sources
Solution Approach 1:
The image is divided into multiple regions based on light source detection, allowing different white balance processing to be applied to each region. This segmentation enables accurate color balance for areas with different light sources while maintaining reasonable processing efficiency through localized operations.
Solution Approach 2:
Different white balance parameters are applied to different regions of the image according to the detected light source type in each region. This local quality approach ensures that each area receives the appropriate color correction for its specific lighting conditions, resolving the color shift problem while maintaining processing efficiency.
2Manufacturing precision
If the image is divided into multiple areas with different processing parameters, then color balance accuracy is improved, but the device complexity increases
Solution Approach 1:
The image processing is segmented into distinct stages: light source detection, region division, and selective parameter application. This structured segmentation manages complexity by breaking down the complex task into manageable, sequential operations that can be implemented efficiently.
Solution Approach 2:
Light source detection and region division are performed as preliminary actions before the actual white balance processing. This preliminary action organizes the image data in advance, allowing the main processing to operate on pre-segmented regions with identified light source characteristics, thereby reducing overall processing complexity.
3Manufacturing precision
If different image processing parameters are applied to each divided area, then image quality is improved, but the processing time increases
Solution Approach 1:
Processing is segmented by light source type rather than by fine-grained regions, reducing the total number of processing operations. Each light source type has predetermined parameters, allowing efficient batch processing of regions with the same lighting characteristics while maintaining high image quality.
Solution Approach 2:
The system uses predetermined parameter sets for different light source types, allowing rapid parameter selection and application. This parameter change approach avoids complex real-time calculations for each region, maintaining fast processing speed while achieving high image quality through appropriate parameter selection for each lighting condition.
Data Source
AI summary
An image processing apparatus includes processing circuitry configured to: divide an input image into a plurality of areas to correspond to predetermined categories, using information of the image; set different image processing parameters for the divided areas corresponding to the predetermined categories, respectively; perform image processing on the divided areas using the image processing parameters corresponding to the divided areas; and combine the divided areas on which the image processing has been performed to form an image.