Multi-Model Neural Filtering for Sequence-Specific Video Coding
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Solution Overview
Problem
Existing video coding techniques using neural network-based filters are not optimized for specific video sequences, leading to suboptimal performance in terms of bitrate and distortion.
Innovation Solution
Implementing multi-model neural network-based filtering techniques that select and apply multiple neural network models based on quantization parameters or explicit signaling in the bitstream to enhance filtering of decoded video data, optimizing it for specific video sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single neural network model is used for filtering decoded video data, then the filtering process is simple and fast, but the performance in terms of bitrate and distortion is suboptimal for specific video sequences
Solution Approach 1:
The system dynamically selects from multiple pre-trained neural network models based on the characteristics of the input video sequence. Instead of using a fixed single model, the encoder evaluates video content properties and chooses the most appropriate model from a set of specialized models, allowing the filtering system to adapt its behavior to different video types while maintaining manageable complexity through selective model application rather than maintaining all models simultaneously active
Solution Approach 2:
The system changes the parameter of model selection by using quantization parameters (QP) or explicit signaling in the bitstream to determine which neural network model to apply. By mapping QP values to specific models or using syntax elements to indicate model indices, the system adapts the filtering approach to match the compression requirements and content characteristics of different video sequences, improving performance without requiring complex real-time model training
2Manufacturing precision
If multiple neural network models are selected and applied based on video sequence characteristics, then bitrate and distortion are reduced for specific video sequences, but the complexity of the filtering process increases
Solution Approach 1:
Multiple neural network models are pre-trained on different types of video content or distortion characteristics before runtime. The encoder prepares a set of specialized models in advance, each optimized for specific video scenarios. During encoding, the system simply selects from this pre-prepared set based on video sequence analysis, avoiding the complexity of training multiple models on-the-fly while still achieving the benefit of specialized filtering for different video types
Solution Approach 2:
The system uses quantization parameter mapping or syntax element signaling to control model selection, leveraging existing video coding parameters to determine which pre-trained model to apply. This approach integrates model selection into the existing encoding workflow without requiring separate complex control mechanisms, thus improving video coding efficiency while keeping the increase in processing complexity manageable
Data Source
AI summary
An example device for filtering decoded video data includes a memory configured to store video data; and one or more processors implemented in circuitry and configured to: decode a picture of video data; code a value for a syntax element representing a neural network model to be used to filter a portion of the decoded picture, the value representing an index into a set of pre-defined neural network models, the index corresponding to the neural network model in the set of pre-defined neural network models; and filter the portion of the decoded picture using the neural network model corresponding to the index.