Depth-Based Auto-Exposure for Image Detail Preservation
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Solution Overview
Problem
Conventional auto-exposure algorithms fail to accurately differentiate between foreground and background content in images, leading to overexposure or underexposure, particularly in scenes with artificial lighting, resulting in loss of detail and improper exposure of important content.
Innovation Solution
A depth-based auto-exposure management system that uses depth maps and object maps to determine an auto-exposure gain, adjusting settings to prioritize important content and account for proximity and illumination fall-off, thereby improving exposure accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional auto-exposure algorithms are used, then the processing is simple and fast, but the exposure accuracy deteriorates for scenes with artificial lighting and depth variations
Solution Approach 1:
The patent segments the image into depth-based regions (foreground and background) using depth maps. Different auto-exposure parameters are applied to different depth regions, allowing accurate exposure control for both close-up foreground content and background content simultaneously, resolving the contradiction between simple processing and accurate exposure.
Solution Approach 2:
The patent applies local quality by assigning different auto-exposure parameters to different spatial regions based on depth information. Foreground content receives different exposure settings than background content, enabling optimized exposure for each region while maintaining overall image quality.
2Measurement precision
If uniform auto-exposure parameters are applied to the entire image, then the processing is simple, but important foreground content becomes overexposed or underexposed
Solution Approach 1:
The patent divides the image into multiple depth-based regions using depth maps. By segmenting the image, the system can apply different auto-exposure parameters to foreground and background regions independently, ensuring accurate exposure for important content while maintaining processing feasibility through automated depth-based classification.
Solution Approach 2:
The patent introduces depth as an additional dimension for organizing image regions. Instead of applying uniform exposure across the entire image, the system uses depth information to create layered exposure control, allowing different exposure parameters for different depth planes.
3Measurement precision
If background content is properly exposed, then background detail is preserved, but foreground content may be overexposed or underexposed
Solution Approach 1:
The patent segments the image into foreground and background regions based on depth information. This segmentation enables independent exposure optimization for each region, preserving detail in both foreground and background content simultaneously by applying appropriate exposure parameters to each segment.
Solution Approach 2:
The patent applies local quality by optimizing exposure parameters specifically for different regions. Background regions receive exposure settings optimized for distance and lighting conditions, while foreground regions receive separate optimization, preventing detail loss in either region.
4Measurement precision
If depth-based auto-exposure management is implemented, then exposure accuracy for foreground content improves, but computational requirements increase
Solution Approach 1:
The patent uses depth maps to segment the image into foreground and background regions. This segmentation enables targeted exposure optimization that improves accuracy for important content while limiting computational energy to only the regions that require enhanced processing based on depth information.
Solution Approach 2:
The patent applies partial action by focusing computational resources on depth-based region differentiation rather than processing the entire image uniformly. By applying exposure optimization only where depth information indicates important content, the system achieves improved accuracy with reduced overall computational energy consumption.
Data Source
AI summary
An illustrative apparatus may obtain a depth map corresponding to an image frame. The image frame may be captured by an image capture system in accordance with an auto-exposure parameter set to a first setting and the depth map may indicate depth values for pixel units of the image frame. The apparatus may also obtain an object map corresponding to the image frame. The object map may indicate which of the pixel units depict an object of a predetermined object type. Based on the depth map and the object map, the apparatus may determine an auto-exposure gain associated with the image frame. Based on the auto-exposure gain, the apparatus may then determine a second setting for the auto-exposure parameter. The second setting may be used by the image capture system to capture subsequent image frames. Corresponding apparatuses, systems, and methods for depth-based auto-exposure are also disclosed.