Neural Codec Proxy for Adaptive Video Encoding Parameters
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Solution Overview
Problem
Existing video codec configurations rely on static or manual tuning, limiting the suitability of neural networks for automated optimization, particularly in machine learning techniques.
Innovation Solution
Implementing a neural network-based proxy within video codecs to dynamically generate control parameters for encoding, utilizing differentiable models and stochastic gradient descent for training, optimizing macroblock sizes and encoding modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static or manual tuning is used for video codec configuration, then device complexity is reduced, but encoding efficiency and adaptability deteriorate
Solution Approach 1:
The system enables self-service by allowing the video codec to automatically configure its own parameters through neural network inference. The neural network model receives input features from the video content and autonomously generates optimal codec control parameters without requiring external manual intervention or complex configuration systems, thus improving encoding efficiency while maintaining relatively simple device architecture.
Solution Approach 2:
The patent replaces manual tuning mechanisms with neural network-based automated parameter generation. Instead of relying on human operators to manually adjust codec parameters based on video characteristics, the system uses machine learning models to automatically infer and generate optimal control parameters, substituting the mechanical/manual configuration process with an intelligent automated system.
2Adaptability or versatility
If manual tuning is used for video codec configuration, then ease of operation is maintained, but adaptability to different encoding applications deteriorates
Solution Approach 1:
The neural network system performs self-service by automatically adapting to different encoding applications without requiring user intervention. The model inferes appropriate codec parameters based on the specific characteristics of each video stream and application requirements, enabling the system to serve itself in configuring optimal settings for diverse scenarios while maintaining ease of operation.
Solution Approach 2:
The system dynamically changes codec control parameters based on inferred video characteristics and application requirements. The neural network generates different parameter sets for different encoding scenarios, allowing the codec to adapt its behavior to match specific application needs while automatically managing the complexity of parameter adjustment.
3Adaptability or versatility
If existing codec components are used without modification, then device complexity is minimized, but suitability for machine learning techniques deteriorates
Solution Approach 1:
The patent implements a dual-mode codec structure that serves multiple functions: it can operate in traditional manual tuning mode and in automated neural network control mode. This universal design allows the same codec infrastructure to support both conventional operation and machine learning-based optimization, enabling the system to adapt to machine learning techniques while maintaining compatibility with existing codec components and minimizing structural complexity.
Data Source
AI summary
Processors, systems and techniques to generate control parameters for a video codec to optimize encoding of video streams is disclosed. In at least one embodiment, a neural network implementing an end-to-end differentiable proxy of the codec, including differentiable models of intra and inter prediction modes, is trained to generate control parameters for the video codec which subsequently encodes respective frames of a data stream.