Feature Substitution for End-to-End Image Compression
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Solution Overview
Problem
Existing end-to-end (E2E) image compression methods face challenges in achieving flexible bitrate control and adaptability to different target metrics, as each deep neural network (DNN) model is typically trained for specific conditions, leading to inefficiencies in storage and computation requirements.
Innovation Solution
A method and apparatus for feature substitution in E2E image compression, utilizing two neural networks - an encoder and a quantizer - trained to determine rate loss based on the bitrate of the compressed representation, allowing for flexible bitrate control and adaptation to different target metrics without the need for multiple models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple DNN models are trained for different bitrates and target metrics, then compression flexibility and adaptability are improved, but storage and computation costs increase significantly
Solution Approach 1:
The patent implements a single universal DNN model that can handle multiple bitrates and target metrics through dynamic quantization. The model is trained once with a unified loss function that incorporates both distortion and rate terms, enabling it to adapt to different compression requirements without requiring separate models for each scenario.
Solution Approach 2:
The patent introduces dynamic quantization where the quantization step size is adjusted based on the desired bitrate and image characteristics. This dynamic adjustment allows the single model to flexibly control compression ratios and adapt to different target metrics, replacing the need for multiple static models trained for specific conditions.
2Adaptability or versatility
If multiple DNN models are trained for different bitrates and target metrics, then compression flexibility and adaptability are improved, but computation costs increase significantly
Solution Approach 1:
The patent implements a single universal DNN model that can handle multiple bitrates and target metrics through dynamic quantization. The model is trained once with a unified loss function that incorporates both distortion and rate terms, enabling it to adapt to different compression requirements without requiring separate models for each scenario.
Solution Approach 2:
The patent changes the quantization parameter dynamically based on the desired bitrate and image characteristics. By adjusting the quantization step size as a controllable parameter, the system achieves different compression levels and optimizes for different target metrics using the same model, thereby reducing computation costs associated with loading and executing multiple specialized models.
3Quantity of substance
If a single DNN model is used for all bitrates and metrics, then storage and computation costs are reduced, but bitrate control precision and adaptability deteriorate
Solution Approach 1:
The patent introduces dynamic quantization where the quantization step size is adjusted based on the desired bitrate and image characteristics. This dynamic adjustment allows the single model to flexibly control compression ratios and adapt to different target metrics, replacing the need for multiple static models trained for specific conditions.
Solution Approach 2:
The patent employs a loss function that provides feedback during training, combining distortion metrics and rate terms. This feedback mechanism enables the single model to learn optimal compression strategies for different bitrates and metrics, maintaining precision in bitrate control despite using a unified model architecture.
4Quantity of substance
If a single DNN model is used for all bitrates and metrics, then storage and computation costs are reduced, but adaptability to different conditions deteriorates
Solution Approach 1:
The patent introduces dynamic quantization where the quantization step size is adjusted based on the desired bitrate and image characteristics. This dynamic adjustment allows the single model to flexibly control compression ratios and adapt to different target metrics, replacing the need for multiple static models trained for specific conditions.
Solution Approach 2:
The patent changes the quantization parameter dynamically based on the desired bitrate and image characteristics. By adjusting the quantization step size as a controllable parameter, the system achieves different compression levels and optimizes for different target metrics using the same model, thereby reducing computation costs associated with loading and executing multiple specialized models.
Data Source
AI summary
A method of feature substitution for end-to-end image compression, is performed by at least one processor and includes encoding an input image, using a first neural network, to generate an encoded representation, and quantizing the generated encoded representation, using a second neural network, to generate a compressed representation. The first neural network and the second neural network are trained by determining a rate loss, based on a bitrate of the generated compressed representation, and updating the generated encoded representation, based on the determined rate loss.