Qualcomm incorporated (20240121398). DIFFUSION-BASED DATA COMPRESSION simplified abstract
Contents
- 1 DIFFUSION-BASED DATA COMPRESSION
DIFFUSION-BASED DATA COMPRESSION
Organization Name
Inventor(s)
Noor Fathima Khanum Mohamed Ghouse of Amsterdam (NL)
Jens Petersen of Amsterdam (NL)
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 20240121398 titled 'DIFFUSION-BASED DATA COMPRESSION
Simplified Explanation
The patent application describes a system and techniques 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 decoder, 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 decoder.
- Residual model is used 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
The 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 efficiently processing image data and improving the quality of reconstructed images by using a residual model with adjustable sampling steps.
Benefits
The benefits of this technology include improved image reconstruction quality, flexibility in adjusting the number of sampling steps, and efficient processing of image data.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of image editing software that offers advanced image reconstruction capabilities.
Possible Prior Art
One possible prior art for this technology could be the use of residual models in image processing and machine learning applications to enhance image reconstruction quality.
What are the specific machine learning models used in this technology?
The specific machine learning models used in this technology are not mentioned in the abstract. It would be helpful to know the types of machine learning models employed to better understand the implementation of the system.
How does the adjustable number of sampling steps impact the final reconstructed image quality?
The abstract does not provide details on how the adjustable number of sampling steps affects the final reconstructed image quality. Understanding this relationship would be crucial in assessing the effectiveness of the technology.
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.
