Hybrid Neural Network Video Encoder with Side Information
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Solution Overview
Problem
Current video encoding techniques face challenges in optimizing coding efficiency, particularly in determining the best mode for encoding video streams, which requires balancing rate and distortion, and are computationally intensive, especially when dealing with large datasets and temporal redundancies in video compression.
Innovation Solution
A neural network-based encoder that receives source data and side information, generates guided information, and outputs it to a decoder for reconstruction, utilizing machine learning to estimate rate and distortion costs, thereby reducing computational complexity and improving coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional video encoding techniques are used to determine the best mode for encoding video streams, then coding efficiency can be optimized, but the computational complexity increases significantly
Solution Approach 1:
The patent replaces conventional mechanical/video encoding algorithms with a neural network-based system. The neural network is trained to predict encoding modes and parameters, substituting the traditional iterative optimization process with a learned mapping function that directly outputs encoding decisions, thereby reducing computational complexity while maintaining coding efficiency
Solution Approach 2:
The neural network is pre-trained on large video datasets to learn optimal encoding strategies beforehand. During actual encoding, the pre-trained network quickly infers encoding parameters without performing complex real-time optimization, effectively performing the computationally intensive work in advance during the training phase
2Device complexity
If neural network-based encoding is used, then computational complexity is reduced, but the ability to handle temporal redundancies in video compression may be insufficient
Solution Approach 1:
The patent merges conventional video encoding techniques with neural network-based encoding. The hybrid system combines the temporal redundancy handling capabilities of traditional video compression algorithms with the computational efficiency of neural networks, allowing both approaches to complement each other's strengths
Solution Approach 2:
The neural network is designed to perform multiple functions: it predicts encoding modes, optimizes compression parameters, and handles temporal redundancy analysis. This multi-functional approach allows a single neural network system to replace multiple specialized components, maintaining comprehensive video compression capabilities while reducing overall system complexity
Data Source
AI summary
A hybrid apparatus for coding a video stream includes a first encoder. The first encoder includes a neural network having at least one hidden layer, and the neural network receives source data from the video stream at a first hidden layer of the at least one hidden layer, receives side information correlated with the source data at the first hidden layer, and generates guided information using the source data and the side information. The first encoder outputs the guided information and the side information for a decoder to reconstruct the source data.