# US 10,504,248 B2 · Machine learning applied to textures compression or upscaling > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,504,248 B2 - Document ID: US10504248B2 - Status: Granted - Kind: B2 - Application number: 15/994,464 - Filed: 2018-05-31 - Published: 2019-10-03 - Granted: 2019-12-10 - Earliest claimed: 2018-03-30 - Assignees: Microsoft Technology Licensing, LLC - Applicants: Microsoft Technology Licensing, LLC - Inventors: Martin Jon Irwin Fuller; Daniel Gilbert Kennett - Web page: https://patentgrep.com/patent/US10504248B2 ## CPC - G06T 11/10: Texturing; Colouring; Generation of textures or colours (retouching, inpainting or scratch removal G06T5/77) - G06F 12/023: {Free address space management} - G06N 20/00: Machine learning - G06T 1/20: Processor architectures; Processor configuration, e.g. pipelining - G06T 9/00: Image coding (bandwidth or redundancy reduction for static pictures H04N1/41; coding or decoding of static colour picture signals H04N1/64; methods or arrangements for coding, decoding, compressing or decompressing digital video signals H04N19/00) - G06T 9/002: {using neural networks} - G06F 2212/401: Compressed data - G06T 2200/28: involving image processing hardware - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## 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. ## Claims ### Claim 1 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. ### Claim 2 (depends on 1) The computer device of claim 1, wherein the hardware compatible compressed textures are in a format of a block compressed number (BCN) texture, and wherein the GPU is further configured to render textures from the hardware compatible compressed textures to present on a display. ### Claim 3 (depends on 1) The computer device of claim 1, wherein the trained machine learning model is trained offline by performing at least one machine learning search on the graphics hardware incompatible compressed textures to determine the configurations for a block compression of the graphics hardware incompatible compressed textures. ### Claim 4 (depends on 3) The computer device of claim 3, wherein different machine learning searches are trained for one or more of different texture types and different content types. ### Claim 5 (depends on 1) The computer device of claim 1, wherein the application is further operable to: access a modified MIP chain that includes a plurality of images with varying resolutions for use with textures of the application, wherein the modified MIP chain removed a highest resolution image from the original MIP chain and the plurality of images correspond to an original MIP chain for use with the textures of the application. ### Claim 6 (depends on 5) The computer device of claim 5, wherein the application is further operable to: reconstruct the highest resolution image by applying the trained machine learning model to a first image of the modified MIP chain; generate a hardware compatible compressed reconstructed MIP chain with the reconstructed highest resolution image and the plurality of images; and transmit the hardware compatible compressed reconstructed MIP chain to the GPU. ### Claim 7 (depends on 6) The computer device of claim 6, wherein the hardware compatible compressed reconstructed MIP chain includes images in a format of a block compressed number (BCN) texture. ### Claim 8 (depends on 5) The computer device of claim 5, wherein the modified MIP chain also removes one or more lower resolution image from the original MIP chain. ### Claim 9 (depends on 1) The computer device of claim 1, wherein at least one of the GPU and a central processing unit (CPU) decompresses the hardware incompatible compressed textures directly into the hardware compatible compressed textures. ### Claim 10 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. ### Claim 11 (depends on 10) The method of claim 10, wherein the hardware compatible compressed textures are in a format of a block compressed number (BCN) texture, and wherein the GPU is further configured to render textures from the hardware compatible compressed textures to present on a display. ### Claim 12 (depends on 10) The method of claim 10, wherein the trained machine learning model is trained offline by performing at least one machine learning search on the graphics hardware incompatible compressed textures to determine the configurations for a block compression of the graphics hardware incompatible compressed textures. ### Claim 13 (depends on 12) The method of claim 12, wherein different machine learning searches are trained for one or more of different texture types and different content types. ### Claim 14 (depends on 10) The method of claim 10, further comprising: accessing a modified MIP chain that includes a plurality of images with varying resolutions for use with textures of the application, wherein the modified MIP chain removed a highest resolution image from the original MIP chain and the plurality of images correspond to an original MIP chain for use with the textures of the application. ### Claim 15 (depends on 14) The method of claim 14, further comprising: reconstructing the highest resolution image by applying the trained machine learning model to a first image of the modified MIP chain; generating a hardware compatible compressed reconstructed MIP chain with the reconstructed highest resolution image and the plurality of images; and transmitting the hardware compatible compressed reconstructed MIP chain to the GPU. ### Claim 16 (depends on 15) The method of claim 15, wherein the hardware compatible compressed reconstructed MIP chain includes images in a format of a block compressed number (BCN) texture. ### Claim 17 (depends on 14) The method of claim 14, wherein the modified MIP chain also removes one or more lower resolution image from the original MIP chain. ### Claim 18 (depends on 10) The method of claim 10, wherein at least one of the GPU and a central processing unit (CPU) decompresses the hardware incompatible compressed textures directly into the hardware compatible compressed textures. ### Claim 19 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.