Google llc (20240098280). Video Coding With Guided Machine Learning Restoration simplified abstract
Contents
Video Coding With Guided Machine Learning Restoration
Organization Name
Inventor(s)
Urvang Joshi of Mountain VIew CA (US)
Sarah Parker of San Francisco CA (US)
Elliott Karpilovsky of Santa Clara CA (US)
Debargha Mukherjee of Cupertino CA (US)
Video Coding With Guided Machine Learning Restoration - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240098280 titled 'Video Coding With Guided Machine Learning Restoration
Simplified Explanation
The abstract describes a method for image coding using guided machine learning restoration. Here is a simplified explanation of the patent application:
- Obtaining reconstructed frame data by decoding
- Obtaining a restored frame by restoring the reconstructed frame
- Outputting the restored frame
- Obtaining the restored frame by obtaining a reconstructed block, guide parameter values, and a restored block
- Including the restored block in the restored frame
- Obtaining the restored block by inputting the reconstructed block to an input layer of a trained guided convolutional neural network
- Obtaining neural network output channel predictions from the output layer
- Obtaining a guided neural network prediction as a linear combination of guide parameter values and neural network output channel predictions
- Generating the restored block using the guided neural network prediction
- Potential Applications
This technology can be applied in video compression, image restoration, and video streaming services.
- Problems Solved
This technology helps in improving the quality of compressed images and videos, reducing artifacts, and enhancing visual fidelity.
- Benefits
The benefits of this technology include better image and video quality, reduced file sizes for storage and transmission, and improved user experience in multimedia applications.
- Potential Commercial Applications
This technology can be used in video streaming platforms, surveillance systems, medical imaging, and satellite imaging services.
- Possible Prior Art
One possible prior art could be traditional image and video compression techniques that do not utilize guided machine learning for restoration.
- Unanswered Questions
- How does this technology compare to existing image and video compression methods?
This article does not provide a direct comparison with existing compression methods, leaving room for further analysis on the performance and efficiency of this technology.
- What are the computational requirements for implementing this guided machine learning restoration in real-time applications?
The article does not delve into the computational aspects of real-time implementation, leaving a gap in understanding the practical feasibility of this technology in real-world scenarios.
Original Abstract Submitted
image coding using guided machine learning restoration may include obtaining reconstructed frame data by decoding, obtaining a restored frame by restoring the reconstructed frame, and outputting the restored frame. obtaining the restored frame may include obtaining a reconstructed block, obtaining guide parameter values, obtaining a restored block, and including the restored block in the restored frame. obtaining the restored block may include inputting the reconstructed block to an input layer of a trained guided convolutional neural network, wherein the neural network is constrained such that an output layer has a defined cardinality of channels, obtaining, from the output layer, neural network output channel predictions, obtaining a guided neural network prediction as a linear combination of the guide parameter values and the neural network output channel predictions, and generating the restored block using the guided neural network prediction.
