Neural Network Compression for Video Bitrate and Quality Trade-offs
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Solution Overview
Problem
Existing data compression techniques for image and video data struggle to balance quality and efficiency, often resulting in high bitrates and computational overhead, especially when dealing with high-quality video content.
Innovation Solution
The use of machine learning systems, specifically neural network-based compression models, that employ implicit neural representations to compress and decompress media data, eliminating the need for storing pre-trained neural networks and reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional video coding techniques are used to compress video data, then bitrate is reduced, but video quality deteriorates
Solution Approach 1:
The patent replaces traditional mechanical video coding techniques with machine learning-based neural network systems. The encoder uses a neural network to learn optimal compression representations, while the decoder uses another neural network to reconstruct video frames. This substitution enables the system to achieve both low bitrate and high video quality by leveraging the pattern recognition and adaptive modeling capabilities of neural networks, overcoming the limitations of conventional coding standards.
Solution Approach 2:
The patent dynamically adjusts neural network parameters (weights and biases) based on the input video content characteristics. The system modifies compression parameters adaptively according to scene complexity, motion intensity, and other video features, allowing optimal balance between bitrate and quality for different video segments. This parameter adaptation enables maintaining high quality while minimizing overall bitrate.
2Speed
If pre-trained neural networks are stored for compression and decompression, then processing speed is improved, but memory requirements increase
Solution Approach 1:
The patent segments the neural network model into multiple components: a shared backbone network and task-specific head networks. The backbone network contains the majority of parameters and is stored once, while multiple lightweight head networks handle different compression and decompression tasks. This segmentation reduces redundant memory storage while maintaining fast processing capability through efficient model architecture design.
Solution Approach 2:
Instead of storing complete duplicate neural networks for both encoding and decoding operations, the patent uses a shared backbone network that is copied or referenced by multiple task-specific heads. This approach eliminates redundant parameter storage while preserving the computational efficiency needed for real-time processing. The system loads the shared backbone once and generates different functional outputs through parameter sharing and selective activation.
3Device complexity
If conventional compression algorithms are used, then device complexity is reduced, but computational overhead increases for high-quality output
Solution Approach 1:
The patent performs preliminary training of neural network models offline using large datasets, capturing complex video patterns and compression strategies in advance. During actual compression operations, the pre-trained models execute efficiently with reduced computational overhead. The system prepares feature extractors, compression models, and reconstruction networks beforehand, so that real-time processing requires only inference rather than intensive training computations.
Data Source
AI summary
Techniques are described for compressing and decompressing data using machine learning systems. An example process can include receiving a plurality of images for compression by a neural network compression system. The process can include determining, based on a first image from the plurality of images, a first plurality of weight values associated with a first model of the neural network compression system. The process can include generating a first bitstream comprising a compressed version of the first plurality of weight values. The process can include outputting the first bitstream for transmission to a receiver.