Quantized Auto-Encoder Neural Network for Image Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image and video compression technologies face challenges in achieving high fidelity while efficiently reducing data size, particularly due to the non-differentiable nature of quantization in deep learning models, which hinders gradient flow and requires additional training time and fidelity loss.
Innovation Solution
A deep-learning-based codec system that employs a quantized auto-encoder neural network with a flexible encoder architecture and fake quantization during training, allowing for gradient flow and dynamic growth of compression blocks based on performance, thereby achieving superior compression ratios with minimal quality loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional lossy compression is applied to reduce image size, then storage space is decreased, but image quality and fidelity are degraded
Solution Approach 1:
The patent replaces traditional mechanical/mathematical compression algorithms with a neural network-based system. The encoder neural network learns optimal compression representations, while the decoder neural network reconstructs images with high fidelity. This substitution of mechanical compression methods with intelligent learning-based methods resolves the contradiction by achieving both small data size and high image quality through learned transformations rather than fixed mathematical operations.
Solution Approach 2:
The patent dynamically adjusts compression parameters based on the specific image content and requirements. The neural network models are trained to adaptively select optimal compression levels, allowing the system to maintain high fidelity when needed while achieving significant compression when appropriate. This dynamic parameter adjustment resolves the static trade-off between compression ratio and quality.
2Quantity of substance
If quantization is applied in deep learning models for compression, then data size is reduced, but gradient flow is blocked hindering training
Solution Approach 1:
The patent introduces an intermediary approach to quantization that maintains gradient flow. Instead of applying hard quantization that blocks gradients, the system uses soft quantization or quantization-aware training techniques where gradients can still flow through the quantization operation during backpropagation. This intermediary solution allows both compression to occur and training to proceed effectively.
3Quantity of substance
If existing deep learning compression models are trained with adjustments for quantization, then compression is achieved, but training time and processing time increase
Solution Approach 1:
The patent performs preliminary training actions to establish robust models before final quantization is applied. By pre-training the neural networks on uncompressed or lightly compressed data and then fine-tuning with quantization-aware techniques, the system achieves good compression ratios without requiring extensive retraining. This preliminary action reduces the overall training time while maintaining compression effectiveness.
4Quantity of substance
If lossy compression is used to decrease storage space, then storage efficiency is improved, but machine learning model performance is negatively affected
Solution Approach 1:
The patent segments the compression process into distinct encoder and decoder neural network components, each optimized for their specific function. The encoder learns to extract essential features for compression, while the decoder learns to reconstruct images that preserve critical information for machine learning tasks. This segmentation allows the system to optimize for both storage efficiency and downstream task performance separately, resolving the contradiction.
Data Source
AI summary
A system and method for compressing and decompressing image data, which provides better compression and minimal representation of the input image with minimal loss compared to previous Deep Learning codecs. The system can provide quantization during training, flexible addition of filters, and conditional complexity of image compression. The Deep Learning codec generates codes that are directly usable with Machine Learning algorithms, thus boosting the performance of Machine Learning algorithms.