Reinforcement Learning Video Encoder Bit Allocation
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Solution Overview
Problem
Existing video encoder bit allocation methods are suboptimal due to empirical weighting of video frames, failing to effectively minimize cumulative distortions under rate constraints, especially with inter-dependencies between frames.
Innovation Solution
A reinforcement learning method for video encoders that computes a state based on frame features and determines bit allocation ratios within a GOP-level bit budget, using a Deep Deterministic Policy Gradient algorithm to optimize bit allocation and minimize distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If empirical weighting methods are used to allocate bits to video frames, then the bit allocation process is simple, but the cumulative distortion is not minimized effectively
Solution Approach 1:
The patent replaces the mechanical/empirical weighting system with a reinforcement learning agent that learns optimal bit allocation policies through interaction with the video encoding environment. The RL agent substitutes traditional empirical weight assignment mechanisms with a data-driven decision-making system that minimizes cumulative distortion more effectively.
Solution Approach 2:
The patent changes the parameters used for bit allocation from fixed empirical weights to dynamic allocations determined by the RL agent based on frame-specific features and state conditions. This allows the system to adapt bit allocation parameters to actual video content characteristics rather than relying on predetermined weightings.
2Productivity
If traditional rate control schemes are used, then the encoding process is computationally efficient, but the GOP-level bit rate control is unstable
Solution Approach 1:
The patent implements feedback mechanisms where the RL agent observes the state of the encoding process including current bit rate, distortion metrics, and frame characteristics, then adjusts bit allocation decisions accordingly. This closed-loop control provides stable GOP-level bit rate management while maintaining computational efficiency through the learned policy.
Solution Approach 2:
The RL agent performs preliminary learning during a training phase to establish optimal bit allocation policies before actual encoding. This preliminary action allows the system to have stable, pre-computed decision rules ready for efficient execution during production encoding without requiring complex real-time computations.
3Manufacturing precision
If reinforcement learning is applied to bit allocation, then the cumulative distortion is minimized more effectively, but the system complexity increases
Solution Approach 1:
The RL agent is self-trained through interaction with the encoding environment, automatically learning optimal bit allocation strategies without requiring manual configuration or complex system architecture. The agent serves itself by learning from experience and adapting to different video content types, reducing the need for complex external control mechanisms.
4Manufacturing precision
If frame-level bit allocation is performed with inter-frame dependencies considered, then the overall video quality is improved, but the decision-making complexity increases
Solution Approach 1:
The patent segments the video stream into individual frames while the RL agent considers inter-frame dependencies through its state representation and reward structure. Each frame's bit allocation is decided independently by the agent based on features that capture temporal relationships, avoiding the complexity of joint optimization while maintaining video quality.
Data Source
AI summary
A reinforcement learning method for frame-level bit allocation is disclosed. The reinforcement learning method includes steps of: (a) at a testing time, computing a state according to a plurality of features; (b) determining an action according to a policy; (c) determining a number of bits allocated to an i-th frame in a group of pictures (GOP) according to the action, a GOP-level bit budget and the state, wherein i is a positive integer; (d) encoding the i-th frame according to the number of bits allocated to the i-th frame in the GOP; and (e) repeating the steps (a)˜(d) until an end of the GOP.