CNN Wavelet Transform Model for Image Encoding
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Solution Overview
Problem
Existing image encoding methods using wavelet transforms face inefficiencies due to the mismatch between the complexity and diversity of natural image features and the simplicity of artificially designed wavelet basis functions, leading to low encoding/decoding performance and increased complexity in selecting optimal directional parameters.
Innovation Solution
Implementing a wavelet transform model based on a convolutional neural network (CNN) for image encoding, which performs multiple wavelet transforms to obtain a more compact transform domain representation, and using an entropy encoding model based on deep neural networks to improve compression ratio and reduce time complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional wavelet transforms with fixed basis functions are used, then the encoding process is simple and fast, but the transform cannot adapt to the complexity and diversity of natural image features, resulting in poor encoding performance
Solution Approach 1:
The patent transforms the static, fixed wavelet basis functions into dynamic, learnable basis functions through neural networks. The wavelet transform model uses trainable parameters that adapt to different image characteristics, allowing the system to dynamically adjust the transform basis according to the input image's complexity and diversity while maintaining efficient encoding performance
Solution Approach 2:
The patent changes the parameters of the wavelet basis functions from fixed predetermined values to learnable parameters optimized through training. By adjusting the basis function parameters based on image features during the training phase, the system achieves better adaptation to natural image diversity while keeping the actual encoding process efficient
2Reliability
If directional wavelets are used to process features in multiple directions, then directional features are improved, but a large quantity of redundant coefficients are generated, reducing compression ratio
Solution Approach 1:
The patent creates a universal wavelet transform model that can handle multiple directional features simultaneously through the neural network's ability to learn complex patterns. Instead of using separate directional wavelets for each orientation, the trained neural network model processes all directional information efficiently in a unified framework, reducing redundant coefficients while maintaining directional feature representation
3Adaptability or versatility
If adaptive directional wavelet with block partitioning is used, then the wavelet basis function adapts to image directional features, but multiple encoding decision processes greatly increase encoding complexity
Solution Approach 1:
The patent performs the complex adaptation work in advance during the training phase. The wavelet transform model is pre-trained on a large dataset to learn optimal basis functions for different image types. During actual encoding, the pre-adapted model is directly applied without requiring real-time decision processes, thus achieving both adaptability to image features and low encoding complexity
Data Source
AI summary
The present disclosure provides an image decoding method based on a wavelet transform. The method includes: performing entropy decoding on the compressed bitstream to obtain a reconstructed wavelet coefficient; dequantizing the reconstructed wavelet coefficient to obtain a dequantized wavelet coefficient; and performing N inverse wavelet transforms on the dequantized wavelet coefficient based on the wavelet transform model to obtain a reconstructed image, where the wavelet transform model is implemented based on a convolutional neural network (CNN).