US 2026/0087680 A1 · REINFORCEMENT LEARNING-BASED RATE CONTROL FOR END-TO-END NEURAL NETWORK BASED VIDEO COMPRESSION

Abstract

An end-to-end neural network-based rate control method based on reinforcement learning implements video codec embodiments. In one embodiment, the codec environment is based on an Asymmetric Gained Variational Auto-Encoder (AG-VAE) architecture. A Reinforcement Learning (RL) agent is implemented through a deep convolutional neural network. In an embodiment, the RL agent conveys a choice of gain vector to the AG-VAE codec and receives reward data from the AG-VAE environment. Rate control is optimized over a period of frames, such as a Group of Pictures (GOP).

Claims21

Dependent claims

A method, comprising:

encoding a portion of video using a determined number of bits;
and,
determining the number of bits to allocate for the encoded portion of video based on a number of frames, wherein said determining comprises using a gain vector from a reinforcement learning agent that uses a latent determined from the encoding.

Dependencies are inferred from the claim text.

Facts

Application

19/114,220Filed 22 Sep 2023

Priority

23 Sep 2022

Published

26 Mar 2026

Term ends

Not estimatedThe source records don’t support an estimate.

Classification13 codes

G06T 9/002
Image coding: using neural networks
H04N 19/147
Data rate or code amount at the encoder output: according to rate distortion criteria
G06N 3/006
Artificial life, i.e. computing arrangements simulating life: based on simulated virtual individual or collective life forms, e.g. social…
G06N 3/0455
Combinations of networks: auto-encoder networks; Encoder-decoder networks

Reinforcement learning-based rate control for end-to-end neural network based video compression

Life of this patent

Reinforcement learning-based rate control for end-to-end neural network based video compression

Life of this patent

Abstract

Claims21