Google llc (20240107079). HIGH-FIDELITY GENERATIVE IMAGE COMPRESSION simplified abstract

HIGH-FIDELITY GENERATIVE IMAGE COMPRESSION

Organization Name

google llc

Inventor(s)

George Dan Toderici of Mountain View CA (US)

Fabian Julius Mentzer of Zürich (CH)

Eirikur Thor Agustsson of Zürich (CH)

Michael Tobias Tschannen of Zürich (CH)

HIGH-FIDELITY GENERATIVE IMAGE COMPRESSION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240107079 titled 'HIGH-FIDELITY GENERATIVE IMAGE COMPRESSION

Simplified Explanation

The patent application describes methods, systems, and apparatus for training an encoder neural network to process data items and output compressed representations. Here are the key points:

• Processing data items with an encoder neural network to generate latent representations
• Determining a conditional entropy model using a hyper-encoder neural network
• Generating compressed representations of the data items
• Reconstructing the data items using a decoder neural network
• Evaluating a loss function and updating encoder network parameters

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1. 1. 1. Potential Applications of this Technology

- Data compression - Image and video processing - Signal processing

1. 1. 1. Problems Solved by this Technology

- Efficient data representation - Improved data storage and transmission - Enhanced data processing speed

1. 1. 1. Benefits of this Technology

- Reduced storage requirements - Faster data transmission - Enhanced data analysis capabilities

1. 1. 1. Potential Commercial Applications of this Technology
         1. Data Compression Technology for Enhanced Storage Solutions

1. 1. 1. Possible Prior Art

There may be prior art related to neural network-based data compression techniques or image/video processing methods that could be relevant to this patent application.

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1. 1. Unanswered Questions

1. 1. 1. How does this technology compare to existing data compression methods?

This article does not provide a direct comparison with traditional data compression techniques such as JPEG or MP3. It would be helpful to understand the performance differences and advantages of this neural network-based approach.

1. 1. 1. What are the potential limitations or challenges of implementing this technology in real-world applications?

The article does not address any potential drawbacks or obstacles that may arise when deploying this technology in practical settings. Understanding these challenges would be crucial for assessing the feasibility of widespread adoption.

Original Abstract Submitted

methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an encoder neural network configured to receive a data item and to process the data item to output a compressed representation of the data item. in one aspect, a method includes, for each training data item: processing the data item using the encoder neural network to generate a latent representation of the training data item; processing the latent representation using a hyper-encoder neural network to determine a conditional entropy model; generating a compressed representation of the training data item; processing the compressed representation using a decoder neural network to generate a reconstruction of the training data item; processing the reconstruction of the training data item using a discriminator neural network to generate a discriminator network output; evaluating a first loss function; and determining an update to the current values of the encoder network parameters.