Differentiable Encoder for Video Processing Model Training
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Solution Overview
Problem
Existing video processing methods lose information during encoding, leading to poor accuracy due to the video processing model not learning distorted information caused by encoding processes.
Innovation Solution
Implement a differentiable encoder in the video processing model that simulates quantization and encoding processes, allowing gradient backpropagation to enhance training accuracy and enable the model to learn encoding distortions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a video processing model is used to process videos before encoding, then video processing accuracy is improved, but information loss during encoding reduces the overall accuracy
Solution Approach 1:
The differentiable encoder is integrated into the video processing model during the training phase, allowing the model to learn encoding distortions in advance. This preliminary action enables the model to compensate for information loss that will occur during actual encoding, thereby maintaining high processing accuracy despite subsequent encoding losses.
Solution Approach 2:
The differentiable encoder acts as an intermediary component between the video processing model and the actual encoding process. It simulates the encoding process during training, providing a bridge that allows the model to learn about encoding distortions without requiring access to the actual encoder's internal operations or ground truth encoded data.
2Measurement precision
If a differentiable encoder is added to simulate quantization and encoding processes, then training accuracy is improved, but model complexity increases
Solution Approach 1:
Instead of using the actual complex encoding process during training, a differentiable copy or approximation of the encoder is created. This copy mimics the essential quantization and encoding behaviors while maintaining mathematical differentiability, allowing gradient flow during training without the full complexity of the real encoder.
Solution Approach 2:
The non-differentiable mechanical encoding process is replaced with a differentiable computational approximation. This substitution allows the use of gradient-based optimization methods during training while preserving the essential characteristics of the encoding process, thereby improving training accuracy without requiring the full complexity of the actual encoder implementation.
Data Source
AI summary
The present disclosure provides a video processing method, apparatus and electronic device. The method includes: acquiring a first video; processing the first video based on a video processing model to obtain a second video, wherein a training stage of the video processing model includes a differentiable encoder which is configured to simulate quantization and encoding processes performed by an encoder on a video, and the differentiable encoder is capable of performing gradient backpropagation; and encoding the second video.