Neural Network Latent Scaling for Variable Bit Rate Compression
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Solution Overview
Problem
Conventional techniques for image compression using neural networks require separate models for different bit rates, leading to computational inefficiency and high storage costs, as they do not adapt to varying compute capabilities and network conditions.
Innovation Solution
A computer-implemented method and apparatus that utilize a learned latent scaling parameter to generate and communicate a latent representation of an input at a variable bit rate, enabling efficient adaptation to different compute capabilities and network conditions through a single bit stream with unequal error protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate neural network models are trained for different bit rates, then multiple bit rates can be provided to adapt to different compute capabilities and network conditions, but computational efficiency deteriorates and storage costs increase
Solution Approach 1:
The patent merges multiple separate neural network models into a single unified model that can operate at multiple bit rates. The encoder processes input data through a shared computation graph, and the bit rate selection is achieved by controlling the number of decoding steps or iterations rather than using separate models, thereby reducing computational overhead and storage requirements while maintaining bit rate adaptability.
Solution Approach 2:
The neural network model is designed to be universal and multi-functional, capable of operating at multiple bit rates through a single model instance. The model can dynamically adjust its output quality and bit rate based on computational resources and network conditions without requiring separate specialized models for each bit rate scenario.
2Adaptability or versatility
If separate neural network models are trained for different bit rates, then multiple bit rates can be provided to adapt to different compute capabilities and network conditions, but storage costs increase
Solution Approach 1:
The patent consolidates multiple bit rate models into a single unified model structure, dramatically reducing the storage requirements. Instead of storing separate weight matrices and model parameters for each bit rate, the system stores one shared model that can generate outputs at different bit rates through controlled processing iterations.
Solution Approach 2:
The unified neural network model serves multiple bit rate functions simultaneously, making the system storage-efficient. A single model instance replaces what would otherwise require multiple model copies, reducing the storage burden while maintaining the ability to adapt to different computational and network conditions.
3Productivity
If a single bit stream is used with unequal error protection, then computational efficiency improves and storage costs reduce, but adaptability to different bit rates deteriorates
Solution Approach 1:
The system implements dynamic bit rate adaptation within a single computational pass. The encoder processes input data once through the neural network, and the decoder can dynamically adjust the number of reconstruction iterations or decoding steps to achieve different effective bit rates, providing adaptability without requiring separate models or multiple encoding passes.
Solution Approach 2:
The patent changes operational parameters (such as the number of decoding iterations, quantization levels, or reconstruction steps) rather than changing the model structure itself to achieve different bit rates. This allows a single model to produce variable bit rate outputs by adjusting processing parameters, maintaining both computational efficiency and bit rate adaptability.
Data Source
AI summary
A computer-implemented method for operating an artificial neural network (ANN) includes receiving an input by the ANN. The ANN generates a latent representation of the input. The latent representation is communicated according to a bit rate based on a learned latent scaling parameter. The latent scaling parameter is learned based on a channel index and a tradeoff parameter value that corresponds to a value that balances the bit rate and a distortion.