Adaptive perceptual quality based camera tuning using reinforcement learning
US12348859B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 13, 2023 |
| Grant date | Jul 1, 2025 |
| Priority date | — |
| Expiry date | Jan 29, 2044 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04N23/611
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods are provided for dynamically tuning camera parameters in a video analytics system to optimize analytics accuracy. A camera captures a current scene, and optimal camera parameter settings are learned and identified for the current scene using a Reinforcement Learning (RL) engine. The learning includes defining a state within the RL engine as a tuple of two vectors: a first representing current camera parameter values and a second representing measured values of frames of the current scene. Quality of frames is estimated using a quality estimator, and camera parameters are adjusted based on the quality estimator and the RL engine for optimization. Effectiveness of tuning is determined using perceptual Image Quality Assessment (IQA) to quantify a quality measure. Camera parameters are adaptively tuned in real-time based on learned optimal camera parameter settings, state, quality measure, and set of actions, to optimize the analytics accuracy for video analytics tasks.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.