Content Capture Device Exposure Control Using Object Weight Arrays
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional automatic exposure control (AEC) methods in content capture devices measure light levels without considering the content of the field of view, leading to suboptimal exposure settings.
Innovation Solution
The techniques involve identifying luma values and objects in an image, computing separate weight arrays for object categories, and combining these to create a total weight array that augments luma values. This weighted luma average is then used to adjust exposure settings based on differences from a target value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional automatic exposure control measures light levels without considering content, then exposure settings can be determined automatically, but the exposure quality becomes suboptimal
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different regions and objects within the image. Instead of uniform exposure control, the system identifies specific objects (faces, eyes, pupils) and applies different exposure weights to each region. For example, eyes and pupils receive higher weights than other facial features, ensuring optimal exposure for the most critical areas while maintaining automated control.
Solution Approach 2:
The patent segments the image into distinct regions based on object identification. By detecting and categorizing different objects (faces, eyes, pupils, other body parts), the system creates separate weight arrays for each object type. This segmentation allows independent exposure optimization for each region while maintaining overall automated exposure control.
2Measurement precision
If separate weight arrays are computed for each object category, then exposure optimization for specific objects is achieved, but computational complexity increases
Solution Approach 1:
The patent merges multiple separate weight arrays into a single combined weight array. After computing individual weight arrays for different object categories (faces, eyes, pupils, other body parts), the system combines these arrays by summing their values pixel-by-pixel. This merging step integrates the exposure optimization benefits of multiple object types while producing a unified exposure control signal, managing computational complexity through efficient combination.
Solution Approach 2:
The patent applies partial action by computing weight arrays only for detected objects rather than processing the entire image uniformly. The system identifies objects first, then creates weight arrays only for regions containing these objects, leaving other regions with default or minimal weights. This partial approach optimizes exposure for critical objects while reducing overall computational burden.
3Measurement precision
If user manual adjustment of exposure settings is used, then exposure quality can be optimized, but reliability and ease of operation deteriorate
Solution Approach 1:
The patent implements self-service by enabling the camera system to automatically determine optimal exposure settings without user intervention. The system uses built-in sensors to detect light levels, identifies objects within the image, computes appropriate weight arrays, and adjusts exposure parameters autonomously. This self-service mechanism eliminates the need for manual user adjustment, improving both reliability and ease of operation while maintaining high exposure quality through intelligent automated control.
Data Source
AI summary
A method includes receiving a first image captured by a content capture device, identifying a first object in the first image and determining a first update to a first setting of the content capture device. The method further includes receiving a second image captured by the content capture device, identifying a second object in the second image, and determining a second update to a second setting of the content capture device. The method further includes updating the first setting of the content capture device using the first update, receiving a third image using the updated first setting of the content capture device, updating the second setting of the content capture device using the second update, receiving a fourth image using the updated second setting of the content capture device, and stitching the third image and the fourth image together to form a composite image.