Conditional Autoencoder Compression With Variable Rate Control
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Solution Overview
Problem
Existing image compression methods require multiple models for different Lagrange multipliers to achieve fine resolution in the rate-distortion curve, which is impractical and inefficient, especially when covering a broad range of compression qualities.
Innovation Solution
A conditional autoencoder network trained with a plurality of Lagrange multipliers and mixed quantization bin sizes, allowing for adaptive compression rate adjustment by varying the Lagrange multiplier and quantization bin size, enabling a single network to perform rate adaptation and produce compressed images of varying quality and rate without re-training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple models are trained separately for different Lagrange multiplier values, then fine resolution in the rate-distortion curve is achieved, but device complexity and computational overhead increase significantly
Solution Approach 1:
The patent merges multiple separate autoencoder models trained for different Lagrange multiplier values into a single unified model. This model takes the Lagrange multiplier as an additional input and dynamically adapts its behavior to achieve the desired compression rate, thereby reducing device complexity while maintaining fine resolution in the rate-distortion curve.
Solution Approach 2:
The unified autoencoder model is designed to perform multiple functions by accepting different Lagrange multiplier values as inputs. This single model can adapt to various compression requirements and produce optimal results across different operating points on the rate-distortion curve, eliminating the need for multiple specialized models.
2Adaptability or versatility
If multiple models are deployed for rate adaptation, then broad range of rate-distortion curve is covered, but ease of operation and deployment become impractical
Solution Approach 1:
The patent introduces dynamic adaptability into the autoencoder by making it conditional on the Lagrange multiplier value. The model dynamically adjusts its internal representations and transformations based on the input Lagrange multiplier, enabling it to cover a broad range of the rate-distortion curve while maintaining ease of deployment through a single static model structure.
Solution Approach 2:
The Lagrange multiplier serves as an intermediary input that mediates between the compression requirements and the autoencoder's processing. By conditioning the model on this parameter, the system achieves versatile rate adaptation without the operational complexity of managing multiple models.
3Device complexity
If a single network is used with fixed Lagrange multiplier, then device complexity is reduced, but adaptability to different compression rates is lost
Solution Approach 1:
The patent enables rate adaptation by changing the Lagrange multiplier parameter that is fed into the single autoencoder network. This parameter change allows the model to adapt to different compression rates and quality requirements while maintaining a fixed, simple network structure, thus resolving the contradiction between device complexity and adaptability.
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 for compression includes receiving a first image and a first scheme as inputs for an autoencoder network; determining a first Lagrange multiplier based on the first scheme; and using the first image and the first Lagrange multiplier as inputs, computing a second image from the autoencoder network. The autoencoder network is trained using a plurality of Lagrange multipliers and a second image as training inputs.