Automatic Zoom Level Adjustment for Image Composition
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Solution Overview
Problem
Current camera systems lack the capability to automatically set the appropriate zoom level for image capture, leading to poor image composition and user dissatisfaction, especially for novice users.
Innovation Solution
A zoom level determination system that identifies foreground and background regions of interest in a digital image stream, adjusts the zoom level to maintain an acceptable margin, and determines a new position to improve image composition, using machine learning to automatically select the optimal zoom setting for the imaging component.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automatic modes are used to set image capture parameters, then image quality is improved, but not all parameters are automatically set requiring user intervention
Solution Approach 1:
The system performs self-service by automatically detecting regions of interest and computing optimal zoom levels without user intervention. The imaging component and processing system autonomously adjust capture parameters based on analyzed image data, eliminating the need for manual zoom setting while maintaining image quality.
Solution Approach 2:
The system changes the zoom level parameter automatically based on detected regions of interest. By computing optimal zoom levels from image analysis and dynamically adjusting this parameter, the system extends automatic control to previously manual settings, improving both ease of operation and image quality.
2Ease of operation
If manual zoom control is used, then user control over composition is maintained, but image composition quality deteriorates for novice users
Solution Approach 1:
The patent replaces manual mechanical zoom control with an automated computational system. Instead of relying on user manipulation of zoom controls, the system uses image analysis algorithms to detect regions of interest and automatically computes optimal zoom levels, substituting human judgment with machine-based composition assistance.
Solution Approach 2:
The system implements feedback by analyzing captured images, identifying regions of interest, and using this information to determine optimal zoom levels. This closed-loop approach allows the system to continuously improve composition quality by adjusting zoom based on actual image content and detected important regions.
3Device complexity
If zoom level is not automatically adjusted, then device complexity is reduced, but image composition and aesthetics worsen
Solution Approach 1:
The system segments the image processing task by identifying specific regions of interest within the full image. By focusing computational resources on detecting and analyzing these key regions rather than processing the entire image uniformly, the system achieves effective zoom level determination with manageable computational complexity.
Data Source
AI summary
A system obtains an image from a digital image stream captured by an imaging component. Both a foreground region of interest and a background region of interest present in the obtained image are identified, and the imaging component is zoomed out as appropriate to maintain a margin (a number of pixels) around both the foreground region of interest and the background region of interest. Additionally, a position of regions of interest (e.g., the background region of interest and the foreground region of interest) to improve the composition or aesthetics of the image is determined, and a composition score of the image indicating how good the determined position is from an aesthetics point of view is determined. A zoom adjustment value is determined based on the position of the regions of interest, and the imaging component is caused to zoom in or out in accordance with the zoom adjustment value.