End-to-End Neural Image Compression With Online Encoder Optimization
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Solution Overview
Problem
Traditional hybrid video codecs are difficult to optimize as a whole, and improving a single module does not result in significant coding gain, necessitating a more efficient end-to-end neural image compression framework.
Innovation Solution
An optimized end-to-end neural image compression (NIC) framework using a trainable module for preprocessing, which tunes the encoder within the framework through online training to achieve better rate-distortion performance without requiring training on the pretrained model itself.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional hybrid video codecs are used, then existing coding standards are maintained, but the system is difficult to optimize as a whole and single module improvements do not result in significant coding gain
Solution Approach 1:
The patent merges multiple compression modules (encoder, decoder, preprocessing, postprocessing) into a unified end-to-end neural network framework. This integration allows the system to optimize the entire pipeline simultaneously rather than individual modules, enabling significant coding gain while maintaining manageable optimization complexity through joint training.
Solution Approach 2:
The neural image compression framework is designed as a universal system that can handle diverse image types and compression requirements through a single unified architecture. The end-to-end framework performs multiple functions (encoding, decoding, preprocessing, postprocessing) within one system, eliminating the need for separate optimization of individual modules and achieving overall performance improvement.
2Productivity
If end-to-end neural image compression framework is implemented, then overall optimization is enabled, but computational costs for recalibration increase
Solution Approach 1:
The framework performs preliminary training and optimization during the development phase, creating a pre-calibrated model that can process new images efficiently without requiring extensive recalibration. This preliminary action establishes the foundational compression parameters once, allowing for low computational cost inference during actual image compression tasks.
Solution Approach 2:
The system optimizes compression parameters through controlled parameter changes during training, establishing an optimal balance between compression ratio and reconstruction quality. By learning the optimal parameter settings during training, the framework avoids the need for expensive real-time recalibration when processing new images, reducing computational costs while maintaining high compression performance.
3Adaptability or versatility
If pretrained model is used, then training data requirements are reduced, but the model cannot be optimized for specific image types without extensive recalibration
Solution Approach 1:
The framework segments the optimization process into two distinct phases: a preliminary training phase where the model learns general compression characteristics from diverse data, and a fine-tuning phase where the model adapts to specific image types with minimal computational cost. This segmentation allows the system to maintain adaptability to different image types while significantly reducing the time required for recalibration compared to full retraining.
Data Source
AI summary
Neural network based image compression using an optimized end-to-end (E2E) neural image compression (NIC) framework being performed by at least one processor and includes receiving an input image to an E2E NIC framework, processing the input image in its entirety using an encoder in the E2E NIC framework to obtain a first bitstream representation of the input image in its entirety, reconstructing an output image from the first bitstream representation of the input image in its entirety using a decoder in the E2E NIC framework, optimizing the encoder in the E2E NIC framework by reducing a distortion loss between the input image and the output image, and processing the input image using the optimized encoder in the E2E NIC framework to obtain a second bitstream representation of the input image. Further, the input may be partitioned into blocks for which encoder optimization is performed for each block and each block is encoded instead of the entire image.