Neural Image Decoder Architecture for Low-Complexity Compression
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Solution Overview
Problem
Existing image and video compression technologies face challenges in optimizing the balance between complexity and coding efficiency, as traditional methods struggle to leverage correlations between coding tools effectively.
Innovation Solution
Implementing a neural image compression (NIC) system with a core decoder and hyper decoder architecture, utilizing neural networks with specific configurations such as sequential upsampling stages, cropping, and activation layers, to achieve a lightweight and efficient decoding process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional coding tools are used individually, then each tool can be optimized separately, but the overall coding performance cannot leverage optimization correlations between coding tools
Solution Approach 1:
The patent merges multiple individual coding tools into a unified end-to-end neural network system. The encoder and decoder are combined into a single trainable model that processes the entire compression pipeline jointly, allowing optimization correlations between different coding stages to be leveraged through holistic training rather than separate tool optimization.
Solution Approach 2:
The neural network system serves multiple coding functions within a single unified architecture. The same network model performs encoding, decoding, and optimization across different coding stages, replacing the need for multiple specialized coding tools while maintaining the ability to leverage inter-tool optimization correlations.
2Reliability
If end-to-end NIC system is implemented, then holistic optimization is achieved, but model complexity and training efficiency must be carefully balanced
Solution Approach 1:
The end-to-end NIC system is segmented into distinct functional modules including encoder, decoder, and hyper decoder components. Each module has a specific function but they work together as a unified trainable system, allowing holistic optimization while managing complexity through modular design that enables targeted training and optimization of individual segments.
Solution Approach 2:
The neural network architecture employs dynamic components that can adapt during training and inference. The model structure allows for flexible parameter adjustment and optimization throughout the training process, enabling the system to balance complexity and performance by dynamically adjusting the level of detail and computation in different operational phases.
3Manufacturing precision
If more upsampling stages are added to the decoder, then image reconstruction quality improves, but inference time and computational complexity increase
Solution Approach 1:
The decoder applies different processing strategies to different regions and stages of the reconstruction process. Rather than uniformly applying high-computation operations throughout, the system uses selective processing where more computational resources are allocated to critical reconstruction stages and regions, while less critical areas use simplified processing, thereby maintaining quality while reducing overall inference time.
Data Source
AI summary
This disclosure relates generally to image coding and particularly to methods and systems for neural image compression (NIC). The disclosed NIC decoder/encoder may include various neural network components that are configured to achieve a balance between network complexity and coding efficiency. Such a NIC decoder/encoder implementation particularly include a core decoder and a hyper decoder each including a neural network architecture adapted for achieving a lightweight decoder/encoder.