Qualcomm incorporated (20240121392). NEURAL-NETWORK MEDIA COMPRESSION USING QUANTIZED ENTROPY CODING DISTRIBUTION PARAMETERS simplified abstract
Contents
- 1 NEURAL-NETWORK MEDIA COMPRESSION USING QUANTIZED ENTROPY CODING DISTRIBUTION PARAMETERS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 NEURAL-NETWORK MEDIA COMPRESSION USING QUANTIZED ENTROPY CODING DISTRIBUTION PARAMETERS - 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
NEURAL-NETWORK MEDIA COMPRESSION USING QUANTIZED ENTROPY CODING DISTRIBUTION PARAMETERS
Organization Name
Inventor(s)
Amir Said of San Diego CA (US)
NEURAL-NETWORK MEDIA COMPRESSION USING QUANTIZED ENTROPY CODING DISTRIBUTION PARAMETERS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240121392 titled 'NEURAL-NETWORK MEDIA COMPRESSION USING QUANTIZED ENTROPY CODING DISTRIBUTION PARAMETERS
Simplified Explanation
The patent application describes entropy coding techniques for media data coded using neural-based techniques. A media coder determines a probability distribution function parameter for a data element of a data stream coded by a neural-based media compression technique, based on the standard deviation of the probability distribution function of the data stream. It then determines a code vector based on this parameter and entropy codes the data element using the code vector.
- Neural-based media compression technique
- Probability distribution function parameter determination
- Code vector generation
- Entropy coding of data element
Potential Applications
This technology can be applied in:
- Video compression
- Image compression
- Audio compression
Problems Solved
This technology helps in:
- Efficient data compression
- Improved data transmission
- Reduced storage requirements
Benefits
The benefits of this technology include:
- Higher compression ratios
- Faster data transmission
- Enhanced data storage efficiency
Potential Commercial Applications
This technology can be utilized in various commercial sectors such as:
- Media streaming services
- Cloud storage providers
- Telecommunication companies
Possible Prior Art
One possible prior art for this technology could be:
- Traditional entropy coding techniques
- Conventional media compression algorithms
Unanswered Questions
How does this technology compare to existing entropy coding methods?
This article does not provide a direct comparison with traditional entropy coding techniques. It would be interesting to see a performance comparison in terms of compression ratios and computational efficiency.
What impact could this technology have on real-time data processing applications?
The article does not discuss the implications of implementing this technology in real-time data processing scenarios. Understanding its potential effects on latency and processing speed would be valuable.
Original Abstract Submitted
this disclosure describes entropy coding techniques for media data coded using neural-based techniques. a media coder is configured to determine a probability distribution function parameter for a data element of a data stream coded by a neural-based media compression technique, wherein the probability distribution function parameter is a function of a standard deviation of a probability distribution function of the data stream, determine a code vector based on the probability distribution function parameter, and entropy code the data element using the code vector.
