Conditional Autoencoder Rate Control With Mixed Quantization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image compression methods require multiple models for different Lagrange multiplier values, making it impractical to achieve fine resolution across a broad range of rate-distortion curves, and are inefficient in adapting compression quality and rate.
Innovation Solution
A conditional autoencoder trained with mixed quantization bin sizes and a Lagrange multiplier, allowing for single network rate adaptation by varying the Lagrange multiplier and quantization bin size to achieve variable compression rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple models are trained separately for different Lagrange multiplier values, then rate adaptation capability is improved, but device complexity and training time increase
Solution Approach 1:
The patent merges multiple rate-adapted models into a single unified model by incorporating the Lagrange multiplier as an additional input channel. This allows the network to adapt to different compression rates dynamically without requiring separate trained models for each rate, thereby reducing device complexity while maintaining rate adaptation capability.
Solution Approach 2:
The unified model serves multiple functions by processing different Lagrange multiplier values through the same network architecture. The model becomes universal, handling various compression rates with a single trained system rather than requiring specialized models for each rate, thus improving versatility without increasing the number of models.
2Adaptability or versatility
If multiple models are trained separately for different Lagrange multiplier values, then coverage of rate-distortion curve is improved, but training time and computational resources increase
Solution Approach 1:
The patent combines multiple training processes into a single training process by integrating the Lagrange multiplier as an input. Instead of training separate models for different rate points, the unified model is trained once to handle the entire rate-distortion curve, significantly reducing training time while maintaining comprehensive coverage.
Solution Approach 2:
The Lagrange multiplier is incorporated as an input channel during the training phase, allowing the model to learn rate adaptation patterns in advance. This preliminary integration enables the model to cover the full rate-distortion curve during a single training process, avoiding the need for multiple sequential training operations.
3Device complexity
If a single model is used with fixed architecture, then device complexity is reduced, but adaptability to different compression rates deteriorates
Solution Approach 1:
The patent introduces dynamics into the previously static model by adding the Lagrange multiplier as a variable input. This allows the single model to dynamically adapt its behavior based on the input Lagrange multiplier value, enabling rate adaptation without requiring multiple fixed-architecture models. The model's effective configuration changes dynamically based on the input parameter.
Solution Approach 2:
The patent enables rate adaptation through parameter changes by using the Lagrange multiplier as a controllable input parameter. By varying this parameter, the model can adjust its compression behavior to achieve different rates, maintaining adaptability while using a single model architecture rather than multiple fixed models.
Data Source
AI summary
A method and apparatus for variable rate compression with a conditional autoencoder is herein provided. According to one embodiment, a method includes training a conditional autoencoder using a Lagrange multiplier and training a neural network that includes the conditional autoencoder with mixed quantization bin sizes.