GPU Auto Focus Using Scene Complexity Thresholds
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Solution Overview
Problem
Current digital camera auto focus systems require user intervention to select the object of interest and introduce delays, as they often focus on areas with the highest focus value rather than the nearest object, leading to inefficient focusing in complex scenes.
Innovation Solution
A system utilizing a graphics processing unit (GPU) to detect movement by calculating pixel-based frame differences and estimating scene complexity, simulating image jitter to derive a frame-specific threshold for judging inter-frame movement, and adjusting focus accordingly, allowing for real-time refocusing on the closest object in a scene.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the camera uses traditional auto focus systems to focus on areas with the highest focus value, then the focusing process is automated, but the system introduces delays and fails to focus on the nearest object in complex scenes
Solution Approach 1:
The system performs preliminary actions by continuously monitoring scene depth and preparing focus adjustments before the user captures the image. The processor actively tracks the nearest object's distance and pre-positions the focus mechanism, so when shooting is initiated, the focus is already or nearly already at the correct distance, eliminating delays associated with post-capture focusing adjustments.
Solution Approach 2:
The patent replaces traditional mechanical focus detection methods with a processor-based system that uses image data and depth information to calculate the nearest object's distance. This substitution of mechanical focus detection with computational analysis enables faster, more accurate focus determination without the delays inherent in mechanical scanning and evaluation processes.
2Ease of operation
If the camera requires user touch screen input to select the object of interest, then the user can control focus selection, but an extra step and delay are introduced
Solution Approach 1:
The system performs self-service by automatically identifying and focusing on the nearest object without requiring user input. The processor independently analyzes the captured image, determines the nearest object based on depth information, and triggers focus adjustment automatically. This eliminates the need for users to manually select focus targets on a touch screen, removing the associated time delay while maintaining appropriate user control through automated intelligent selection.
3Extent of automation
If the camera focuses on areas with the greatest focus value, then the focusing is objective and automated, but the focus value magnitude does not help identify objects of interest in complex scenes
Solution Approach 1:
The system changes the critical parameter from focus value magnitude to object distance. Instead of selecting the area with the highest focus value, the processor uses depth information to identify the nearest object in the scene. This parameter change fundamentally alters the selection criterion, enabling the system to focus on the most relevant object (the nearest one) rather than relying on ambiguous focus value measurements that do not correlate with object interest.
Data Source
AI summary
Disclosed are systems and methods for focusing a digital camera to capture a scene. It is first detected that a change in a scene requires the digital camera to be focused or refocused. The camera focus is automatically scanned from a closest settable distance to farther distances until a first closest object in the scene is detected. Once the first closest object in the scene is detected, the camera focus is set at the distance of the first closest object and a finer scan is executed. In an embodiment, a dynamic movement threshold is calculated and used to determine that the change in scene requires the digital camera to be focused or refocused.