Synthetic Depth-of-Field Interface for Visual Media Editing
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Solution Overview
Problem
Existing techniques for altering visual media on electronic devices are cumbersome and inefficient, often requiring complex user interfaces and consuming excessive time and device energy.
Innovation Solution
The implementation of faster and more efficient methods and interfaces for altering visual content, including the application of a synthetic depth-of-field effect to emphasize specific subjects within media, thereby reducing cognitive burden and conserving power in battery-operated devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing techniques are used to alter visual media, then the editing function is achieved, but the user interface becomes complex and time-consuming
Solution Approach 1:
The system automatically detects subjects in the video and applies depth-of-field effects without requiring manual user intervention. The device serves itself by autonomously identifying the first subject, determining its movement, and adjusting the synthetic aperture accordingly, eliminating the need for complex user operations.
Solution Approach 2:
The system performs preliminary analysis of video frames to identify subjects and their movement patterns before applying the depth-of-field effect. By pre-processing the video data to detect subject motion, the system prepares the necessary information in advance, enabling automatic and efficient effect application without time-consuming manual steps.
2Use of energy by moving object
If existing techniques are used to alter visual media, then the editing function is achieved, but device energy consumption increases
Solution Approach 1:
The system automatically manages the entire depth-of-field effect application process without requiring energy-intensive manual user operations. The device autonomously processes video frames, detects subject movement, and adjusts effects accordingly, reducing overall energy consumption while maintaining high editing efficiency.
Solution Approach 2:
The system dynamically adjusts the synthetic aperture parameters based on detected subject movement. By changing the aperture size parameter in response to subject motion, the system optimizes processing efficiency and reduces energy consumption compared to static or manually-adjusted parameters.
3Ease of operation
If a synthetic depth-of-field effect is applied to emphasize moving subjects, then subject emphasis is improved, but processing complexity increases
Solution Approach 1:
The system automatically detects and tracks subjects in video frames, determining their movement relative to the camera. This self-service approach to subject identification and motion detection simplifies the user interface while maintaining effective subject emphasis through dynamic depth-of-field adjustment.
Solution Approach 2:
The synthetic aperture size is dynamically adjusted based on the detected movement of the first subject. As the subject moves within the video frames, the system automatically modifies the aperture parameter to maintain optimal emphasis, creating a dynamic response that improves subject emphasis without requiring complex manual controls.
Data Source
AI summary
The present disclosure generally relates to user interfaces for altering visual media. In some embodiments, user interfaces capturing visual media (e.g., via a synthetic depth-of-field effect), playing back visual media (e.g., via a synthetic depth-of-field effect), editing visual media (e.g., that has a synthetic depth-of-field effect applied), and/or managing media capture.