Task-Adaptive Pre-Processing for Neural Image Compression
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Solution Overview
Problem
Existing neural image compression (NIC) methods face challenges in flexible bitrate and quality control, as they require training multiple model instances for each desired Rate-Distortion (R-D) trade-off or quality metric, limiting their adaptability to different tasks.
Innovation Solution
The Task-Adaptive Pre-Processing (TAPP) framework generates a substitutional image using a TAPP neural network, encodes it with a first neural network, and updates the image based on rate-distortion loss gradients to adapt to various compression tasks, allowing for flexible bitrate and quality control without needing separate model instances for each task.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional NIC methods train multiple model instances for each desired R-D trade-off or quality metric, then compression performance for specific tasks is improved, but device complexity and training time increase significantly
Solution Approach 1:
The patent implements a universal NIC model that can perform multiple compression tasks with different R-D trade-offs and quality metrics through a single model instance. The model uses task-adaptive pre-processing and gradient-based optimization to dynamically adjust its behavior for different compression objectives, eliminating the need for multiple specialized model instances while maintaining compression performance across various tasks
Solution Approach 2:
The patent changes the operational parameters of a single NIC model dynamically based on the target task. By using gradient computation from R-D loss and quality metric losses, the model adjusts its pre-processing and encoding parameters adaptively for different compression scenarios, allowing one model to serve multiple functions that previously required multiple fixed models
2Manufacturing precision
If traditional NIC methods train separate model instances for each target bitrate or quality loss, then task-specific compression accuracy is improved, but adaptability to new tasks deteriorates
Solution Approach 1:
The patent transforms the static, task-specific models into a dynamic system where a single NIC model can adapt its behavior based on the target task. The model uses gradient-based optimization during pre-processing to dynamically adjust its encoding strategy for different R-D trade-offs and quality metrics, enabling both high compression accuracy for specific tasks and flexibility for new tasks without retraining entire model instances
3Productivity
If multiple model instances are trained for different R-D trade-offs, then compression efficiency for each specific task is improved, but training time and computational resources increase
Solution Approach 1:
The patent performs task-adaptive pre-processing that prepares the input image and optimizes the encoding parameters before the actual compression task. By computing gradients of the R-D loss and quality metric losses in advance and using them to adjust the pre-processed image, the model achieves high compression efficiency for the target task while avoiding the need for time-consuming training of multiple model instances
Data Source
AI summary
A method of task-adaptive pre-processing (TAPP) for neural image compression is performed by at least one processor and includes generating a substitutional image, based on an input image, using a TAPP neural network, and encoding the generated substitutional image to generate a compressed representation, using a first neural network. The TAPP neural network is trained by generating a substitutional training image, based on an input training image, using the TAPP neural network, encoding the generated substitutional training image to generate a compressed training representation, using the first neural network, decoding the generated compressed training representation to reconstruct an output training image, using a second neural network, generating gradients of a rate-distortion (R-D) loss that is generated based on the input training image, the reconstructed output training image and the generated compressed training representation, and updating the generated substitutional training image, based on the generated gradients of the R-D loss.