Block-wise Neural Network Parameter Updates for Video Compression
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Solution Overview
Problem
Current video coding technologies face limitations in efficiently compressing video data, particularly in reducing redundancy and achieving optimal rate-distortion performance, due to the complexity of optimizing hybrid video codecs and the need for adaptive compression strategies that account for varying content.
Innovation Solution
The implementation of block-wise content-adaptive online training in neural image compression (NIC) frameworks, which updates neural network parameters based on specific blocks within an image to optimize rate-distortion performance and improve compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If traditional video coding technologies are used, then bandwidth and storage space requirements are reduced, but compression efficiency and rate-distortion performance are limited
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 fixed mechanical transformation rules with adaptive intelligent processing, thereby improving compression efficiency while maintaining reduced bandwidth and storage requirements
Solution Approach 2:
The patent dynamically adjusts neural network parameters based on content characteristics. By changing parameters adaptively according to the input video content, the system optimizes compression performance for different scenarios, achieving better rate-distortion tradeoffs without increasing overall bandwidth or storage requirements
2Productivity
If hybrid video codecs are used, then compression performance is improved, but system complexity increases
Solution Approach 1:
The patent divides the video coding process into distinct functional modules within the neural network architecture, including separate encoding and decoding networks. This segmentation allows each module to be optimized independently while maintaining overall system performance, reducing the complexity burden of the hybrid approach
Solution Approach 2:
The patent designs a universal neural network framework that can handle multiple video coding tasks through a single trained model. The neural network performs both compression and decompression functions, eliminating the need for separate specialized algorithms and reducing overall system complexity despite improved compression performance
3Ease of operation
If fixed compression strategies are used, then encoding process is simple, but rate-distortion performance is suboptimal for varying content
Solution Approach 1:
The patent transforms the static fixed compression strategy into a dynamic adaptive system. The neural network automatically adjusts its processing based on the characteristics of the input content, enabling optimal rate-distortion performance for varying content while maintaining ease of operation through automated decision-making without manual intervention
Data Source
AI summary
Aspects of the disclosure provide a method, an apparatus, and a non-transitory computer-readable storage medium for video decoding. The apparatus can include processing circuitry. The processing circuitry is configured to decode first neural network update information in a coded bitstream for a first neural network in the video decoder. The first neural network is configured with first pretrained parameters. The first neural network update information corresponds to a first block in an image to be reconstructed and indicates a first replacement parameter corresponding to a first pretrained parameter in the first pretrained parameters. The processing circuitry is configured to update the first neural network in the video decoder based on the first replacement parameter. The processing circuitry can decode the first block based on the updated first neural network for the first block.