DIFFUSION-BASED DATA COMPRESSION

Organization Name

QUALCOMM Incorporated

Inventor(s)

Noor Fathima Khanum Mohamed Ghouse of Amsterdam (NL)

Jens Petersen of Amsterdam (NL)

Tianlin Xu of London (GB)

Guillaume Konrad Sautiere of Amsterdam (NL)

Auke Joris Wiggers of Amsterdam (NL)

DIFFUSION-BASED DATA COMPRESSION - A simplified explanation of the abstract

This abstract first appeared for US patent application 18458006 titled 'DIFFUSION-BASED DATA COMPRESSION

Simplified Explanation

The patent application describes a system for processing image data using a residual model with an adjustable number of sampling steps. The process involves obtaining a latent representation of an image, generating an initial reconstructed image using a machine learning model, and predicting a residual over multiple sampling steps to obtain a final reconstructed image.

Latent representation of an image is obtained.
Initial reconstructed image is generated using a machine learning model.
Residual model processes the initial reconstructed image and noise data to predict a residual over multiple sampling steps.
Final residual representing the difference between the image and the initial reconstructed image is obtained.
Initial reconstructed image and the final residual are combined to generate a final reconstructed image.

Potential Applications

This technology can be applied in image processing, computer vision, and machine learning applications where high-quality image reconstruction is required.

Problems Solved

This technology solves the problem of accurately reconstructing images from noisy or incomplete data, improving the overall quality of image processing tasks.

Benefits

The benefits of this technology include improved image reconstruction accuracy, flexibility in adjusting the number of sampling steps, and enhanced performance in various image processing applications.

Potential Commercial Applications

The potential commercial applications of this technology include image editing software, medical imaging systems, surveillance systems, and quality control in manufacturing processes.

Possible Prior Art

One possible prior art for this technology could be the use of residual models in image processing tasks to improve reconstruction quality and efficiency.

Unanswered Questions

How does the adjustable number of sampling steps impact the final reconstructed image quality?

The patent application does not provide specific details on how the adjustable number of sampling steps affects the final reconstructed image quality. Further research or experimentation may be needed to determine the optimal number of sampling steps for different types of images and noise levels.

What are the computational requirements for implementing this technology in real-time applications?

The patent application does not address the computational requirements for real-time implementation of this technology. Understanding the computational resources needed for efficient processing in real-time scenarios is crucial for practical applications.

Original Abstract Submitted

Systems and techniques are described for processing image data using a residual model that can be configured with an adjustable number of sampling steps. For example, a process can include obtaining a latent representation of an image and processing, using a decoder of a machine learning model, the latent representation of the image to generate an initial reconstructed image. The process can further include processing, using the residual model, the initial reconstructed image and noise data to predict a plurality of predictions of a residual over a number of sampling steps. The residual represents a difference between the image and the initial reconstructed image. The process can include obtaining, from the plurality of predictions of the residual, a final residual representing the difference between the image and the initial reconstructed image. The process can further include combining the initial reconstructed image and the residual to generate a final reconstructed image.