Canon kabushiki kaisha (20240242481). 4:2:0 PACKING OF FEATURE MAPS simplified abstract
Contents
4:2:0 PACKING OF FEATURE MAPS
Organization Name
Inventor(s)
Christopher James Rosewarne of Concord (AU)
4:2:0 PACKING OF FEATURE MAPS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240242481 titled '4:2:0 PACKING OF FEATURE MAPS
Simplified Explanation: This patent application describes a method for decoding feature maps from encoded data by determining feature maps based on samples arranged in two-dimensional arrays.
Key Features and Innovation:
- Decoding feature maps from encoded data
- Determining feature maps based on samples from two-dimensional arrays
- Utilizing a first group of samples in a first two-dimensional array and a second group of samples in a different second two-dimensional array
Potential Applications: This technology could be applied in image processing, computer vision, and data compression systems.
Problems Solved: This technology addresses the challenge of efficiently decoding feature maps from encoded data.
Benefits:
- Improved decoding of feature maps
- Enhanced image processing capabilities
- Increased efficiency in data compression
Commercial Applications: Title: Advanced Image Processing Technology for Data Compression Systems This technology could be utilized in industries such as healthcare (medical imaging), security (surveillance systems), and entertainment (video streaming platforms).
Prior Art: Researchers can explore prior art related to image processing, data compression, and computer vision technologies.
Frequently Updated Research: Researchers are constantly developing new methods for decoding feature maps and improving image processing algorithms.
Questions about Image Processing Technology: 1. How does this technology improve data compression systems? 2. What are the potential applications of this technology in computer vision systems?
Original Abstract Submitted
a method of decoding feature maps from encoded data. a plurality of samples is decoded from the encoded data. the feature maps are determined based on one image from at least a first group of samples arranged in a first two-dimensional array and a second group of samples arranged in a second two-dimensional array, where the second two-dimensional array is different from the first two-dimensional array.
