Neural Video Compression Using Adaptive Parameter Subspaces
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Solution Overview
Problem
Existing video compression techniques, both a priori defined and machine learning-based, face challenges in efficiently compressing diverse video content while maintaining high quality, often requiring substantial computational resources and resulting in increased bandwidth and storage demands.
Innovation Solution
Utilizing machine learning systems, specifically neural networks trained within a subspace of model parameters, to adaptively compress and decompress video data, reducing the need for large neural networks and minimizing bitrate by selecting updated parameters from a lower-dimensional subspace.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional video coding techniques are used to compress video data, then bandwidth and storage requirements are reduced, but video quality and fidelity deteriorate
Solution Approach 1:
The patent transforms the compression approach by changing from fixed coding parameters to adaptive neural network parameters. The system determines updated model parameters based on input video data characteristics, allowing the compression model to adapt its parameters dynamically. This enables maintaining high video quality while achieving effective compression by optimizing parameters specifically for each video content type.
Solution Approach 2:
The patent introduces dynamic adaptability into the compression system by selecting model parameters from a subspace based on the specific video input. Rather than using static compression parameters, the system dynamically adjusts the neural network model parameters to match the characteristics of the video being compressed, thereby maintaining quality while reducing data volume.
2Productivity
If neural network compression systems are trained with full model parameters to achieve instance-adaptive compression, then compression performance improves, but computational resources and memory requirements increase substantially
Solution Approach 1:
The patent extracts only the essential and relevant model parameters from the full neural network parameter set. By identifying and selecting only the parameters that are most important for the specific video compression task from a subspace, the system achieves instance-adaptive compression performance while dramatically reducing the computational resources and memory required compared to using complete model parameters.
Solution Approach 2:
The patent applies partial action by using a subset of model parameters rather than the complete parameter set. The system determines updated parameters from a subspace, which represents a partial selection of the full parameter space. This partial parameter approach provides sufficient compression performance for each video instance while avoiding the excessive computational burden of processing all parameters.
3Manufacturing precision
If high-fidelity video data is transmitted to meet consumer demands for quality and resolution, then video quality improves, but network bandwidth requirements increase significantly
Solution Approach 1:
The patent achieves efficient bandwidth utilization by dynamically changing compression parameters based on video content characteristics. The system analyzes the input video data and adjusts the neural network model parameters accordingly, enabling high-fidelity reconstruction at lower bitrates by optimizing the compression approach for each specific video type and quality requirement.
Data Source
AI summary
Techniques are described for compressing data using machine learning systems. An example process can include receiving input data for compression by a neural network compression system. The process can include determining, based on the input data, a set of updated model parameters for the neural network compression system, wherein the set of updated model parameters is selected from a subspace of model parameters. The process can include generating at least one bitstream including a compressed version of the input data and a compressed version of one or more subspace coordinates that correspond to the set of updated model parameters. The process can include outputting the at least one bitstream for transmission to a receiver.