Dynamic Machine-Learning Autoencoder for Variable-Quality Compression
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Solution Overview
Problem
Neural networks with fixed bottleneck capacities struggle to efficiently encode and decode content of varying complexity, as smaller bottlenecks are optimal for simple content while larger bottlenecks are needed for complex content.
Innovation Solution
A machine-learned autoencoder is configured in a multiplexer format with a branching structure, allowing the bottleneck to be adjusted based on content complexity, using a quality signal to select appropriate paths and sub-networks for encoding and decoding, and trained with loss functions to optimize compression and reconstruction quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed bottleneck capacity is used in the neural network, then the device complexity is reduced, but the adaptability to different content complexities deteriorates
Solution Approach 1:
The patent implements dynamic bottleneck adjustment by introducing a quality signal that controls the activation of different bottleneck capacities during encoding and decoding. The system transitions from a fixed bottleneck architecture to a dynamic one where the bottleneck capacity adapts based on the quality signal, allowing the same network to efficiently handle both simple and complex content by selectively activating appropriate bottleneck sizes.
2Manufacturing precision
If a larger bottleneck capacity is used, then the reconstruction quality for complex content is improved, but the compression efficiency for simple content deteriorates
Solution Approach 1:
The patent applies partial action by selectively activating only the necessary bottleneck capacity required for each encoding task based on the quality signal. Instead of always using a large bottleneck capacity, the system activates only the appropriate subset of bottleneck neurons needed for the current content complexity and quality requirements, thereby avoiding unnecessary computational overhead while maintaining reconstruction quality.
3Loss of energy
If a smaller bottleneck capacity is used, then the compression efficiency is improved, but the reconstruction quality for complex content deteriorates
Solution Approach 1:
The patent creates a universal encoding/decoding system that can handle multiple content complexities and quality requirements using the same network architecture. By implementing multiple bottleneck capacities within a single network and using quality signals to select appropriate paths, the system achieves multi-functionality, allowing one network to serve both simple and complex content encoding/decoding tasks effectively.
4Adaptability or versatility
If multiple bottleneck capacities are implemented, then the adaptability to different content complexities is improved, but the device complexity increases
Solution Approach 1:
The patent segments the bottleneck into multiple capacity levels (e.g., first bottleneck capacity, second bottleneck capacity) with different computational complexities. Each segment corresponds to different quality requirements and content types. The quality signal determines which segment to activate, effectively dividing the encoding/decoding task into manageable parts that can be selectively executed based on requirements.
Data Source
AI summary
An autoencoder is configured to encode content at different quality levels. The autoencoder includes an encoding system and a decoding system with neural network layers forming an encoder network and a decoder network. The encoder network and decoder network are configured to include branching paths through the networks that include different subnetworks. During deployment, content is provided to the encoding system with a quality signal indicating a quality at which the content can be reconstructed. The quality signal determines which of the paths through the encoder network are activated for encoding the content into one or more tensors, which are compressed into a bitstream and later used by the decoding system to reconstruct the content. The autoencoder is trained by randomly or systematically selecting different combinations of tensors to use to encode content and backpropagating error values from loss functions through the network paths associated with the selected tensors.