Neural Network Video Encoder Supervisory Signal Adaptation
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Solution Overview
Problem
Current video coding technologies fail to adapt effectively to the differing quality metrics required for human versus machine consumption of compressed data, leading to suboptimal performance in machine-driven analysis tasks.
Innovation Solution
A neural network-based video coding system that uses a supervisory signal to control the operation of encoders and decoders, allowing for task-specific feature selection and adaptation, enabling efficient compression and decompression for machine consumption while maintaining quality for human perception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional video coding technologies are used, then compression efficiency is achieved, but quality metrics for machine consumption are not optimized
Solution Approach 1:
The system dynamically adapts the neural network encoder and decoder operations based on task-specific supervisory signals. The encoder can switch between different encoding modes (e.g., human-oriented vs. machine-oriented) by receiving different supervisory signals, allowing the system to optimize for either human perceptual quality or machine analysis accuracy depending on the application scenario.
Solution Approach 2:
The supervisory signal mechanism changes the operational parameters of the neural network encoder and decoder. By adjusting the supervisory signal content (e.g., task type, importance weights), the system modifies the feature extraction and reconstruction processes to prioritize different quality metrics, thereby optimizing machine consumption quality without sacrificing compression efficiency.
2Measurement precision
If video coding is optimized for human perception, then human perceptual quality is maintained, but machine-driven analysis tasks suffer from suboptimal performance
Solution Approach 1:
The neural network-based video coding system serves multiple functions through a single unified architecture. The same encoder and decoder can be adapted for both human-oriented video quality optimization and machine-oriented task optimization by simply changing the supervisory signal. This multi-functionality allows the system to maintain human perceptual quality when needed while also optimizing machine analysis performance when required.
Solution Approach 2:
The supervisory signal acts as a feedback mechanism that guides the neural network encoder and decoder operations. By providing task-specific feedback signals (e.g., indicating whether the priority is human quality or machine analysis), the system adjusts its behavior to satisfy different quality requirements, ensuring both human perceptual quality and machine-driven analysis performance are maintained in their respective contexts.
3Device complexity
If a single video coding system is used for both human and machine consumption, then system simplicity is maintained, but task-specific optimization is lost
Solution Approach 1:
The supervisory signal serves as an intermediary that bridges the gap between a single unified video coding system and multiple task-specific optimization requirements. Instead of requiring separate coding systems for different tasks, the supervisory signal mediates the adaptation process, allowing one system to efficiently switch between different optimization goals (human quality, machine analysis, compression efficiency) based on the current task requirements.
Data Source
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AI summary
The embodiments relate to a method comprising receiving media data as an input; controlling a neural network based encoder or a neural network based decoder by using a supervisory signal in order to adapt the operation of the neural network based encoder or the neural network based decoder; generating an encoded representation of the media data on the neural network based encoder, the encoded representation of the media data comprising one or more features relating to the media data; and recovering the media data from the encoded representation on the neural network based decoder and performing one or more tasks on the recovered media data.