Canon kabushiki kaisha (20240205402). GROUPED FEATURE MAP QUANTISATION simplified abstract
Contents
- 1 GROUPED FEATURE MAP QUANTISATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GROUPED FEATURE MAP QUANTISATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Commercial Applications
- 1.9 Questions about Feature Map Decoding Technology
- 1.10 Original Abstract Submitted
GROUPED FEATURE MAP QUANTISATION
Organization Name
Inventor(s)
Christopher James Rosewarne of Concord (AU)
GROUPED FEATURE MAP QUANTISATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240205402 titled 'GROUPED FEATURE MAP QUANTISATION
Simplified Explanation
This patent application describes a method for decoding feature maps from encoded data by determining a parameter related to quantization and performing inverse quantization on the samples to decode the feature maps. The method involves sharing a parameter among a group of samples corresponding to multiple feature maps.
- The method involves decoding feature maps from encoded data.
- A parameter related to quantization is determined.
- Inverse quantization is performed on the samples to decode the feature maps.
- A parameter is shared among a group of samples corresponding to multiple feature maps.
Potential Applications
This technology can be applied in image and video processing, pattern recognition, machine learning, and artificial intelligence systems where feature maps need to be decoded from encoded data efficiently.
Problems Solved
This method addresses the challenge of efficiently decoding feature maps from encoded data while ensuring accurate reconstruction of the original information.
Benefits
- Improved efficiency in decoding feature maps from encoded data - Enhanced accuracy in reconstructing feature maps - Potential for faster processing of large datasets
Commercial Applications
Title: Efficient Feature Map Decoding Technology for Image and Video Processing This technology can be utilized in industries such as computer vision, autonomous vehicles, surveillance systems, and medical imaging for faster and more accurate decoding of feature maps from encoded data.
Questions about Feature Map Decoding Technology
What are the key benefits of using this method for decoding feature maps?
The key benefits include improved efficiency, enhanced accuracy, and faster processing of large datasets.
How does sharing a parameter among samples help in decoding feature maps more effectively?
Sharing a parameter among samples allows for a more streamlined and efficient decoding process, reducing redundancy and optimizing resource utilization.
Original Abstract Submitted
a method of decoding feature maps from encoded data. a parameter related to quantisation is determined. according to the parameter, inverse quantisation is performed for samples decoded from the encoded data to decode the feature maps. for at least a part of the samples decoded from the encoded data, one parameter is shared by a group of samples corresponding to a plurality of feature maps.
