Nvidia corporation (20240114170). FEATURE RECONSTRUCTION USING NEURAL NETWORKS FOR VIDEO STREAMING SYSTEMS AND APPLICATIONS simplified abstract
Contents
- 1 FEATURE RECONSTRUCTION USING NEURAL NETWORKS FOR VIDEO STREAMING SYSTEMS AND APPLICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 FEATURE RECONSTRUCTION USING NEURAL NETWORKS FOR VIDEO STREAMING SYSTEMS AND APPLICATIONS - 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 Original Abstract Submitted
FEATURE RECONSTRUCTION USING NEURAL NETWORKS FOR VIDEO STREAMING SYSTEMS AND APPLICATIONS
Organization Name
Inventor(s)
Aurobinda Maharana of Chinchwad (IN)
FEATURE RECONSTRUCTION USING NEURAL NETWORKS FOR VIDEO STREAMING SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240114170 titled 'FEATURE RECONSTRUCTION USING NEURAL NETWORKS FOR VIDEO STREAMING SYSTEMS AND APPLICATIONS
Simplified Explanation
The patent application relates to facial video encoding and reconstruction in ultra-low bandwidth settings, where a video conferencing or streaming application uses automatically tracked feature cropping information to dynamically determine the size of a bounding shape for maintaining proportion in feature reconstruction.
- Automatically tracked feature cropping information is used in video encoding and reconstruction.
- The size of the bounding shape for identifying the cropped region varies dynamically to maintain proportion for feature reconstruction.
- The tracking scheme can smooth sudden movements and generate natural transitions between frames.
- Tracking and cropping information may be embedded in an encoded bitstream as supplemental enhancement information for eventual decoding by a receiver.
Potential Applications
This technology can be applied in video conferencing, streaming applications, virtual reality, augmented reality, and telemedicine.
Problems Solved
1. Maintaining proportion in feature reconstruction in ultra-low bandwidth settings. 2. Smoothing sudden movements and generating natural transitions between frames.
Benefits
1. Improved video quality in low bandwidth settings. 2. Enhanced user experience in video conferencing and streaming applications. 3. Efficient use of bandwidth for transmitting facial video data.
Potential Commercial Applications
"Facial Video Encoding and Reconstruction Technology for Ultra-Low Bandwidth Settings" can be utilized in video conferencing software, streaming platforms, virtual reality applications, telemedicine services, and surveillance systems.
Possible Prior Art
One possible prior art could be the use of facial recognition technology in video encoding and reconstruction to enhance video quality and user experience.
Unanswered Questions
How does this technology impact data privacy and security in video conferencing applications?
This article does not address the potential implications of embedding tracking and cropping information in encoded bitstreams on data privacy and security concerns in video conferencing applications.
What are the computational requirements for implementing this technology in real-time video streaming applications?
The article does not provide information on the computational resources needed to track and crop facial features in real-time video streaming applications.
Original Abstract Submitted
systems and methods relate to facial video encoding and reconstruction, particularly in ultra-low bandwidth settings. in embodiments, a video conferencing or other streaming application uses automatically tracked feature cropping information. a bounding shape size—used to identify the cropped region—varies and is dynamically determined to maintain a proportion for feature reconstruction, such as resizing in the event of a zoom-in on a face (or other feature of interest) or a zoom-out. the tracking scheme may be used to smooth sudden movements, including lateral ones, to generate more natural transitions between frames. tracking and cropping information (e.g., size and position of the cropped region) may be embedded within an encoded bitstream as supplemental enhancement information (“sei”), for eventual decoding by a receiver and for compositing a decoded face at a proper location in the applicable stream.
