Neural Image Compression Online Training Scaling Factors Offsets
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Solution Overview
Problem
Current video coding technologies face challenges in efficiently compressing video data, particularly in adapting to varying content characteristics, which can lead to suboptimal rate-distortion performance.
Innovation Solution
The implementation of a content-adaptive online training method for neural image compression (NIC) that updates pretrained parameters using scaling factors and offsets, allowing the neural network to finely tune its weights and biases based on specific image content, thereby improving coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional video coding technologies are used, then device complexity is reduced, but rate-distortion performance deteriorates due to inability to adapt to varying content characteristics
Solution Approach 1:
The patent implements dynamic adaptation of neural network parameters (scaling factors and offsets) based on content characteristics. The system transitions from static pre-trained parameters to dynamic content-adaptive parameters, allowing the neural network to adjust its behavior according to the specific content being encoded, thereby improving rate-distortion performance without requiring complete retraining.
Solution Approach 2:
The patent changes the parameters of the neural network (scaling factors and offsets) based on content characteristics. Instead of modifying the entire network architecture or retraining all weights, the system selectively adjusts specific parameters to adapt to different content types, achieving performance improvement with controlled complexity increase.
2Adaptability or versatility
If neural network parameters are fixed and pre-trained, then device complexity is reduced, but adaptability to different content characteristics deteriorates
Solution Approach 1:
The patent performs preliminary training of the neural network offline to establish a base model with pre-trained parameters. This preliminary action prepares the network for various content types, and then enables efficient online adaptation through simple parameter adjustments (scaling and offsets) rather than complete retraining, balancing adaptability with computational efficiency.
Solution Approach 2:
The patent applies partial adaptation by only adjusting specific parameters (scaling factors and offsets) rather than retraining the entire neural network. This partial action provides sufficient content adaptability while avoiding the excessive computational complexity of full network retraining, achieving a practical compromise between adaptability and complexity.
3Productivity
If compression ratio is increased through lossy compression, then bandwidth and storage requirements are reduced, but distortion increases
Solution Approach 1:
The patent incorporates a feedback mechanism where the neural network evaluates the reconstructed image quality and adjusts its parameters accordingly. The network uses the reconstructed output to refine its scaling factors and offsets, creating a closed-loop system that optimizes the balance between compression ratio and reconstruction quality based on actual performance.
Solution Approach 2:
The patent dynamically changes the neural network parameters (scaling factors and offsets) to optimize the trade-off between compression efficiency and reconstruction quality. By adjusting these parameters based on content characteristics and performance feedback, the system achieves better compression ratios while maintaining acceptable distortion levels for different content types.
Data Source
AI summary
Aspects of the disclosure provide methods, apparatuses, and a non-transitory computer-readable storage medium for video encoding and video decoding. An apparatus for video decoding can include processing circuitry. The processing circuitry is configured to decode neural network update information in a coded bitstream for at least one neural network in the video decoder. The at least one neural network is configured with a set of pretrained parameters, and the neural network update information indicates a first modification parameter. The processing circuitry is configured to update the set of pretrained parameters in the at least one neural network in the video decoder based on the first modification parameter. The processing circuitry is configured to decode an encoded image based on the updated at least one neural network.