Samsung electronics co., ltd. (20240187573). METHOD AND APPARATUS WITH NEURAL CODEC simplified abstract
Contents
- 1 METHOD AND APPARATUS WITH NEURAL CODEC
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND APPARATUS WITH NEURAL CODEC - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
METHOD AND APPARATUS WITH NEURAL CODEC
Organization Name
Inventor(s)
Seunghoon Jee of Suwon-si (KR)
METHOD AND APPARATUS WITH NEURAL CODEC - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240187573 titled 'METHOD AND APPARATUS WITH NEURAL CODEC
Simplified Explanation
The neural codec described in the abstract utilizes two simulated predictors to predict blocks within a current frame based on neighboring blocks and reference frames. A selection network then chooses the predicted block based on the prediction mode.
- The neural codec includes a first simulated predictor for predicting a block based on neighboring blocks and a second simulated predictor for predicting a block based on reference frames.
- A selection network chooses the predicted block based on the prediction mode.
Potential Applications
This technology could be applied in video compression algorithms, image processing, and real-time video streaming.
Problems Solved
This technology helps improve video compression efficiency, reduce bandwidth usage, and enhance video quality.
Benefits
The neural codec can lead to faster video encoding and decoding, better compression ratios, and improved video streaming performance.
Potential Commercial Applications
The technology could be used in video streaming services, video conferencing platforms, and surveillance systems.
Possible Prior Art
One possible prior art could be traditional video compression algorithms that do not utilize neural networks for prediction and selection processes.
Unanswered Questions
How does the neural codec handle different types of video content, such as high motion scenes or static images?
The abstract does not provide information on how the neural codec adapts to different types of video content to optimize prediction and selection processes.
What is the computational complexity of the neural codec compared to traditional video compression algorithms?
The abstract does not mention the computational resources required to implement the neural codec and how it compares to existing video compression methods in terms of complexity.
Original Abstract Submitted
a neural codec includes a first simulated predictor for predicting a first block corresponding to a target block within a current frame, wherein the first block is predicted in accordance with the target block of the to-be-predicted current frame and pixels of neighbor blocks adjacent to the target block being input to the first simulated predictor, a second simulated predictor for predicting a second block corresponding to a target block using a reference block of a frame determined based on a prediction mode, wherein the second block is predicted in accordance with the reference block of a reference frame adjacent to the current frame and the target block being input to the second simulated predictor, and a selection network configured to select, based on the prediction mode, one of the first block and the second block as a predicted block.
