Neural Image Compression End-to-End Framework Optimization
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Solution Overview
Problem
Traditional hybrid video codecs are difficult to optimize as a whole, and improvements in one module may not result in a coding gain in overall performance, limiting the effectiveness of existing video coding technologies.
Innovation Solution
The method involves using a neural network-based end-to-end (E2E) neural image compression (NIC) framework that receives an input image, determines a substitute image based on a training model, encodes the substitute image to generate a bitstream, and maps the substitute image to the bitstream to generate a compressed representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional hybrid video codec optimization is applied, then individual module performance can be improved, but overall system performance does not improve due to difficulty in holistic optimization
Solution Approach 1:
The patent merges multiple separate modules (encoder, decoder, and optimization components) into a unified end-to-end neural network framework. This allows the entire compression system to be trained and optimized as a single integrated model, enabling holistic performance improvement rather than isolated module optimization.
Solution Approach 2:
The neural network framework is designed to perform multiple functions simultaneously: encoding, decoding, and optimization within a single unified architecture. This multi-functional approach allows the system to achieve overall performance improvement through joint optimization of all components.
2Manufacturing precision
If substitutional end-to-end neural image compression is implemented, then rate-distortion performance improves, but computational complexity increases due to neural network processing
Solution Approach 1:
The patent replaces traditional mechanical/video coding algorithms with a neural network-based system. The neural network learns optimal compression strategies through training, substituting complex hand-crafted algorithms with a trained model that achieves better rate-distortion performance while managing computational requirements through efficient architecture design.
3Productivity
If end-to-end neural network optimization is applied, then compression efficiency improves, but training and processing time increases
Solution Approach 1:
The patent performs the computationally intensive optimization work during the training phase, where the neural network learns compression strategies in advance. Once trained, the model can efficiently compress images without requiring real-time re-optimization, thus achieving high compression efficiency during actual use while concentrating the time investment in the preliminary training stage.
Data Source
AI summary
Neural network based substitutional end-to-end (E2E) image compression (NIC) being performed by at least one processor and includes receiving an input image to an E2E NIC framework, determining a substitute image based on a training model of the E2E NIC framework, encoding the substitute image to generate a bitstream, mapping the substitute image to the bitstream to generate a compressed representation of the input image. Further, the input may be partitioned into blocks for which a substitute representation is determined for each block and each block is encoded instead of the entire substitute image.