Google llc (20240104786). DATA COMPRESSION USING INTEGER NEURAL NETWORKS simplified abstract
Contents
- 1 DATA COMPRESSION USING INTEGER NEURAL NETWORKS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DATA COMPRESSION USING INTEGER NEURAL NETWORKS - 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
DATA COMPRESSION USING INTEGER NEURAL NETWORKS
Organization Name
Inventor(s)
Nicholas Johnston of San Jose CA (US)
Johannes Balle of San Francisco CA (US)
DATA COMPRESSION USING INTEGER NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104786 titled 'DATA COMPRESSION USING INTEGER NEURAL NETWORKS
Simplified Explanation
The patent application describes a method for data compression and decompression using integer neural networks.
- Data compression and decompression method using integer neural networks:
* The method involves processing data components using integer representations and latent variables with an integer neural network to generate a probability distribution over possible code symbols. * This approach allows for reliable data compression and decompression across various hardware and software platforms.
Potential Applications
The technology can be applied in various fields such as telecommunications, data storage, image and video processing, and artificial intelligence.
Problems Solved
The technology addresses the challenge of efficiently compressing and decompressing data while maintaining reliability and compatibility across different systems.
Benefits
- Improved data compression and decompression efficiency
- Compatibility across a wide range of hardware and software platforms
- Enhanced reliability in data processing and transmission
Potential Commercial Applications
- Data compression software for businesses
- Image and video processing applications
- Telecommunications systems optimization
Possible Prior Art
One potential prior art in data compression and decompression techniques is the use of traditional algorithms like Huffman coding and Lempel-Ziv compression. However, the use of integer neural networks for this purpose is a novel approach that offers unique advantages in terms of efficiency and reliability.
Unanswered Questions
How does this method compare to existing data compression techniques in terms of compression ratio and processing speed?
The article does not provide a direct comparison with traditional compression methods, so it is unclear how the performance of this method stacks up against established techniques.
What are the potential limitations or drawbacks of using integer neural networks for data compression and decompression?
The article does not discuss any potential limitations or challenges that may arise from implementing this technology, leaving room for further exploration of its practical implications.
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reliably performing data compression and data decompression across a wide variety of hardware and software platforms by using integer neural networks. in one aspect, there is provided a method for entropy encoding data which defines a sequence comprising a plurality of components, the method comprising: for each component of the plurality of components: processing an input comprising: (i) a respective integer representation of each of one or more components of the data which precede the component in the sequence, (ii) an integer representation of one or more respective latent variables characterizing the data, or (iii) both, using an integer neural network to generate data defining a probability distribution over the predetermined set of possible code symbols for the component of the data.
