Machine learning applied to textures compression or upscaling
AssigneeMicrosoft Technology Licensing, LLC
InventorsMartin Jon Irwin Fuller, Daniel Gilbert Kennett
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Abstract
Methods and devices for generating hardware compatible compressed textures may include accessing, at runtime of an application program, graphics hardware incompatible compressed textures in a format incompatible with a graphics processing unit (GPU). The methods and devices may include converting the graphics hardware incompatible compressed textures directly into hardware compatible compressed textures usable by the GPU using a trained machine learning model.
Claims19
A computer device, comprising:
- a graphics processing unit (GPU);
- a memory to store data and instructions including an application and graphics hardware incompatible compressed textures in a format incompatible with the GPU;
- at least one processor in communication with the memory;
- an operating system in communication with the memory, the at least one processor, the GPU, and the application, wherein the application is operable to:
- receive, at runtime or installation of the application, the graphics hardware incompatible compressed textures;
- determine that the graphics hardware incompatible compressed textures are incompatible with the GPU;
- and
- convert the graphics hardware incompatible compressed textures directly into hardware compatible compressed textures usable by the GPU using a trained machine learning model, wherein the trained machine learning model uses metadata that provides configurations for a block compression of the graphics hardware incompatible compressed textures to use during the conversion so that the hardware compatible compressed textures closely resemble original raw images of the application.
A method for generating hardware compatible compressed textures, comprising:
- receiving, by an application executing on the computer device, at runtime or installation of an application program, graphics hardware incompatible compressed textures in a format incompatible with a graphics processing unit (GPU);
- determining that the graphics hardware incompatible compressed textures are incompatible with the GPU;
- and
- converting the graphics hardware incompatible compressed textures directly into hardware compatible compressed textures usable by the GPU using a trained machine learning model, wherein the trained machine learning model uses metadata that provides configurations for a block compression of the graphics hardware incompatible compressed textures to use during the conversion so that the hardware compatible compressed textures closely resemble original raw images of the application.
A non-transitory computer-readable medium storing instructions executable by a computer device, comprising:
- at least one instruction for causing the computer device to receive, at runtime of an application program, graphics hardware incompatible compressed textures in a format incompatible with a graphics processing unit (GPU);
- at least one instruction for causing the computer device to determine that the graphics hardware incompatible compressed textures are incompatible with the GPU;
- and
- at least one instruction for causing the computer device to convert the hardware incompatible compressed textures directly into hardware compatible compressed textures usable by the GPU using a trained machine learning model, wherein the trained machine learning model uses metadata that provides configurations for a block compression of the graphics hardware incompatible compressed textures to use during the conversion so that the hardware compatible compressed textures closely resemble original raw images of the application.
Dependencies are inferred from the claim text.