18534073. NEURAL-NETWORK MEDIA COMPRESSION USING QUANTIZED ENTROPY CODING DISTRIBUTION PARAMETERS simplified abstract (QUALCOMM Incorporated)
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 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 18534073 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. The coder 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 elements
Potential Applications
This technology can be applied in various fields such as:
- Image and video compression
- Audio compression
- Data transmission and storage
Problems Solved
This technology helps in:
- Efficient compression of media data
- Reducing storage and bandwidth requirements
- Improving data transmission speeds
Benefits
The benefits of this technology include:
- Higher compression ratios
- Improved quality of compressed media
- Faster data transmission and processing speeds
Potential Commercial Applications
This technology can be utilized in:
- Media streaming services
- Cloud storage solutions
- Communication networks
Possible Prior Art
One possible prior art in this field is the use of traditional entropy coding techniques in media compression algorithms.
Unanswered Questions
How does this technology compare to existing entropy coding methods in terms of compression efficiency?
This article does not provide a direct comparison with existing entropy coding methods to evaluate the compression efficiency of the proposed technique. Further research or comparative studies may be needed to address this question.
What impact does the choice of probability distribution function have on the overall performance of the neural-based media compression technique?
The article does not delve into the specific impact of different probability distribution functions on the performance of the compression technique. Investigating the influence of various distribution functions on compression efficiency could provide valuable insights into optimizing the system.
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.
