Apple inc. (20240106976). Non-Linear Timelapse Videos Based on Multi-Temporal Scale Detection of Scene Content Change simplified abstract
Contents
- 1 Non-Linear Timelapse Videos Based on Multi-Temporal Scale Detection of Scene Content Change
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Non-Linear Timelapse Videos Based on Multi-Temporal Scale Detection of Scene Content Change - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does this technology impact battery life in devices capturing timelapse videos?
- 1.11 Are there any limitations to the types of scenes or motion patterns that this technology can effectively optimize for timelapse videos?
- 1.12 Original Abstract Submitted
Non-Linear Timelapse Videos Based on Multi-Temporal Scale Detection of Scene Content Change
Organization Name
Inventor(s)
Christophe Seyve of Saratoga CA (US)
Xuemei Zhang of Mountain View CA (US)
Non-Linear Timelapse Videos Based on Multi-Temporal Scale Detection of Scene Content Change - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240106976 titled 'Non-Linear Timelapse Videos Based on Multi-Temporal Scale Detection of Scene Content Change
Simplified Explanation
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).
- Intelligent determinations of non-linear image recording rates for timelapse videos
- Applicable to videos captured over long durations with varying amounts of motion/scene content change
- Smoothly varying image recording rate based on scene content change estimates
- Improves quality of timelapse videos by avoiding long stretches with little action and rapid action that is hard to perceive
Potential Applications
This technology could be applied in various fields such as:
- Film production
- Surveillance systems
- Traffic monitoring
- Sports analysis
Problems Solved
The technology addresses the following issues:
- Inconsistent image recording rates in timelapse videos
- Difficulty in capturing long-duration videos with varying scene content changes
- Ensuring viewer engagement by optimizing image recording rates
Benefits
The benefits of this technology include:
- Enhanced quality of timelapse videos
- Improved viewer experience
- Better utilization of storage space by avoiding unnecessary footage
Potential Commercial Applications
This technology has potential commercial applications in:
- Camera and video equipment manufacturing
- Software development for video editing
- Entertainment industry for film production
Possible Prior Art
One possible prior art in this field is the use of algorithms to adjust frame rates in videos based on scene content changes. However, the specific technique of smoothly varying image recording rates in timelapse videos based on multi-temporal scale estimates may be a novel approach.
Unanswered Questions
How does this technology impact battery life in devices capturing timelapse videos?
The article does not address the potential impact of this technology on the battery life of devices used for capturing timelapse videos. It would be important to understand if the intelligent determinations of image recording rates have any significant effect on power consumption.
Are there any limitations to the types of scenes or motion patterns that this technology can effectively optimize for timelapse videos?
The article does not mention any limitations or specific scenarios where this technology may not be as effective. It would be valuable to explore if there are certain types of scenes or motion patterns that could pose challenges for the intelligent determinations of image recording rates.
Original Abstract Submitted
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).
