Sparse Decoder Fine-Tuning for Single-Image Deep Coding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing end-to-end trainable deep models for image compression, while improving rate-distortion trade-offs for videos, are not optimal for single images due to increased code size from weight updates and require expensive hyperparameter optimization.
Innovation Solution
Implement structural sparsity by fine-tuning only a selected subset of decoder parameters, either identical or image-specific, and use a new training procedure that optimizes the rate-distortion ratio without hyperparameters, incorporating trainable quantization for weight updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If end-to-end trainable deep models are used for image compression, then rate-distortion performance is improved, but code size increases due to weight updates
Solution Approach 1:
The patent segments the decoder parameters into a full parameter set and a selected subset of parameters to be fine-tuned. By dividing the parameters and only updating the selected subset, the system reduces the amount of data that needs to be transmitted while maintaining improved rate-distortion performance.
Solution Approach 2:
Instead of updating all decoder parameters (full action), the patent applies partial action by fine-tuning only a selected subset of parameters. This partial update approach reduces the code size while still achieving the desired rate-distortion improvement.
2Reliability
If all decoder parameters are fine-tuned, then rate-distortion performance is optimized, but computational cost and time increase
Solution Approach 1:
The patent segments the parameter update process by identifying and updating only a selected subset of parameters rather than all parameters. This segmentation reduces the computational burden and fine-tuning time while maintaining effective rate-distortion optimization.
Solution Approach 2:
The patent applies partial action by performing fine-tuning on only the necessary subset of parameters. This partial fine-tuning approach significantly reduces computational cost and time compared to updating all parameters, while still achieving optimal rate-distortion performance.
3Reliability
If hyperparameter optimization is performed, then model performance is tuned, but computational complexity and time increase
Solution Approach 1:
The patent employs self-service by using a training procedure that automatically determines the selected subset of parameters and their updates without requiring external hyperparameter optimization. The model self-adjusts based on the input image and decoded embedding, eliminating the need for complex hyperparameter tuning.
Solution Approach 2:
The patent changes the approach from fixed hyperparameter optimization to dynamic parameter selection. The selected subset of parameters is determined based on the input image characteristics, allowing the system to adaptively adjust parameters without requiring complex hyperparameter optimization procedures.
Data Source
AI summary
A deep neural network-based coding system for images determines update parameters of a deep neural network model for decoding an image. The parameters are determined by an encoder and provided to a decoder to update the model of the decoder before decoding the image. This provides structural sparsity by fine-tuning only some parameters of the neural decoder. The update is done either on a set of predetermined parameters so that the structural sparsity is identical for all images or on a set of parameters selected based on the image to be encoded so that the structural sparsity is image specific. A new training procedure as well as an end-to-end trainable quantization are also proposed allowing to include trained parameters in a bitstream and to update parameters in the decoder.