Dynamic Timelapse Recording Rate Adjustment
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Solution Overview
Problem
Existing methods for creating timelapse videos often result in unpleasing visuals due to constant image frame recording rates, leading to stretches with little action or excessive action, making it difficult for viewers to perceive the content, especially when capturing videos over long durations with varying device motion and scene changes.
Innovation Solution
The implementation of a device with processors that dynamically adjust image recording rates based on multi-temporal scale scene content change detectors, using image statistics such as luma histograms, motion vectors, and semantic content information to vary the recording rate, ensuring a smoother and more engaging timelapse video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a constant image frame recording rate is used, then the recording process is simple, but the timelapse video quality deteriorates with long stretches of inaction or excessive action
Solution Approach 1:
The patent implements dynamic adjustment of the image recording rate based on detected scene content changes. The system transitions from a static constant recording rate to a dynamic variable recording rate that adapts to scene activity, using multi-temporal scale detectors to monitor scene changes and adjust the recording rate accordingly, thereby resolving the contradiction between operational simplicity and video quality.
Solution Approach 2:
The system changes the recording rate parameter dynamically based on scene content analysis. By monitoring scene activity levels and adjusting the recording rate parameter in response to detected changes, the system optimizes video quality without requiring complex manual intervention, thus resolving the contradiction between simplicity and quality.
2Device complexity
If a constant image frame recording rate is used, then the device complexity is low, but the ability to adapt to varying scene content deteriorates
Solution Approach 1:
The system performs self-service by automatically detecting scene content changes and adjusting its own recording rate without external intervention. The multi-temporal scale detectors and control logic enable the recording system to autonomously adapt to varying scene conditions, resolving the contradiction between device complexity and adaptability through automated self-adjustment.
Solution Approach 2:
The system implements feedback mechanisms where scene content detection results are fed back to control the recording rate. The multi-temporal scale detectors continuously monitor scene changes and provide feedback signals that trigger adjustments in the recording rate, enabling the system to adapt to varying content while maintaining manageable complexity through automated closed-loop control.
3Loss of information
If the video captures long durations to ensure complete action coverage, then the content completeness is improved, but the video length increases making it less shareable
Solution Approach 1:
The system extracts and records only the essential portions of the scene that contain meaningful action or content changes. By using multi-temporal scale detectors to identify significant scene changes and recording primarily during these periods, the system extracts the core content while minimizing unnecessary recording, thus reducing video duration while maintaining action coverage completeness.
Solution Approach 2:
The system applies partial recording action by recording at full rate only during periods of significant scene change, rather than continuously recording at constant high rate throughout the entire duration. This partial action approach captures essential content while significantly reducing overall recording time and final video length, resolving the contradiction between completeness and duration.
Data Source
AI summary
Devices, methods, and non-transitory program storage devices are disclosed herein to perform intelligent determinations of non-linear (i.e., dynamic) image recording rates for the production of improved timelapse videos. The techniques described herein may be especially applicable to timelapse videos captured over long durations of time and/or with varying amounts of device motion/scene content change over the course of the captured video (e.g., when a user is walking, exercising, driving, etc. during the video's capture). By smoothly varying the image recording rate of the timelapse video in accordance with multi-temporal scale estimates of scene content change, the quality of the produced timelapse video may be improved (e.g., fewer long stretches of the video with too little action, as well as fewer stretches of the video where there is so much rapid action in the timelapse video that it is difficult for a viewer to perceive what is happening in the video).