Detecting visual artifacts in image sequences using a neural network model
US11836597B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 29, 2019 |
| Grant date | Dec 5, 2023 |
| Priority date | — |
| Expiry date | Mar 18, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Motivated by the ability of humans to quickly and accurately detect visual artifacts in images, a neural network model is trained to identify and locate visual artifacts in a sequence of rendered images without comparing the sequence of rendered images against a ground truth reference. Examples of visual artifacts include aliasing, blurriness, mosaicking, and overexposure. The neural network model provides a useful fully-automated tool for evaluating the quality of images produced by rendering systems. The neural network model may be trained to evaluate the quality of images for video processing, encoding, and/or compression techniques. In an embodiment, the sequence includes at least four images corresponding to a video or animation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.