/Machine Learning Techniques To Create Higher Resolution Compressed Data Structures Representing Textures From Lower Resolution Compressed Data Structures And Training Therefor
Abstract
Machine learning is used to generate a first mipmap of a texture having a first compression based on a second mipmap of the same texture and having a second compression without using compression or decompression in generating the first mipmap. The first mipmap can then be used to render a computer graphics object. Training can be done using as input decompressed blocks from originally compressed blocks.
Full Text
What is claimed is:
Machine learning is used to generate a first mipmap of a texture having a first compression based on a second mipmap of the same texture and having a second compression without using compression or decompression in generating the first mipmap. The first mipmap can then be used to render a computer graphics object. Training can be done using as input decompressed blocks from originally compressed blocks.
Timeline
Filed
05/19/2026Published
09/17/2026Granted
Not AvailableIPC Codes(3)
G06T 3/4053:based on super-resolution, i.e. the output image resolution being higher than the sensor resolution
G06N 20/00:Machine learning
G06T 9/00:Image coding (bandwidth or redundancy reduction for static pictures H04N 1/41; coding or decoding of static colour picture signals H04N 1/64; methods or arrangements for coding, decoding, compressing or decompressing digital video signals H04N 19/00)