Visual Media Interfaces with Automatic Depth-of-Field Tracking
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Solution Overview
Problem
Existing techniques for altering visual media on electronic devices are cumbersome and inefficient, requiring multiple key presses and consuming excessive time and energy, particularly in battery-operated devices.
Innovation Solution
Implementing a method that applies a synthetic depth-of-field effect to captured video frames to emphasize a subject, reducing the need for manual adjustments and conserving power by dynamically changing the effect as the subject moves within the field-of-view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing techniques are used to alter visual media, then the visual content can be modified, but the process is cumbersome and time-consuming requiring multiple key presses
Solution Approach 1:
The system automatically detects the subject in the video and applies depth-of-field effects without requiring manual user input. The processor autonomously analyzes video frames, identifies the subject, and adjusts visual effects accordingly, eliminating the need for multiple key presses and manual adjustments.
Solution Approach 2:
The patent replaces manual mechanical operations (key presses, manual focus adjustments) with automated computational processing. The system uses image processing algorithms and machine learning models to automatically detect subjects and apply depth-of-field effects, substituting physical user interactions with digital automation.
2Ease of operation
If existing techniques are used to alter visual media, then the visual content can be modified, but excessive device energy is consumed
Solution Approach 1:
The system performs automated subject detection and depth-of-field application without requiring continuous user input, reducing the energy associated with repeated manual operations. The once-through automated processing is more energy-efficient than multiple manual adjustment cycles.
Solution Approach 2:
The system performs subject detection and depth-of-field parameter determination in advance during video capture, rather than requiring post-capture manual adjustments. This preliminary automated action reduces subsequent energy consumption by eliminating the need for repeated processing during user interactions.
3Productivity
If existing techniques are used to alter visual media, then the visual content can be modified, but the cognitive burden on the user is increased
Solution Approach 1:
The system autonomously performs subject detection, tracking, and depth-of-field application without requiring the user to understand or control these processes. The user simply captures video, and the system handles all subsequent visual effects automatically, significantly reducing cognitive burden.
Solution Approach 2:
The patent extracts the complex cognitive tasks of subject identification and depth-of-field adjustment from the user and transfers them to the automated system. The user is relieved of these mentally demanding tasks, which are instead performed by the processor using automated algorithms.
4Manufacturing precision
If manual adjustments are required to emphasize subjects in video, then focus can be controlled, but redundant user inputs are needed
Solution Approach 1:
The system automatically detects and tracks the subject through multiple video frames, continuously adjusting depth-of-field effects to maintain precise subject emphasis without requiring repeated user inputs. The automated subject tracking ensures precision while eliminating redundancy.
Solution Approach 2:
The system continuously analyzes video frames to detect subject position and adjusts depth-of-field effects in real-time based on this feedback. This closed-loop automated control maintains precise subject emphasis dynamically, replacing the need for multiple manual adjustment inputs.
Data Source
Figure 1~1A
Figure 1B
Figure 2
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.