End-to-End Video Compression Model Single Loss Function
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Solution Overview
Problem
Current video compression methods are inefficient due to the use of multiple deep learning modules with different loss functions, leading to uncontrollable loss values and suboptimal video quality at predetermined code rates.
Innovation Solution
An end-to-end model trained with a single loss function is used for video compression, reducing redundant calculations and ensuring consistent quality by calculating loss values uniformly across the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple deep learning modules with different loss functions are used for video compression, then the compression can be performed with modular flexibility, but the loss values become uncontrollable and video quality deteriorates at predetermined code rates
Solution Approach 1:
The patent merges multiple separate deep learning modules with different loss functions into a unified end-to-end model with a single loss function. This integration allows for consistent optimization across all compression stages, preventing loss value accumulation and ensuring predictable video quality at predetermined code rates while maintaining compression flexibility.
2Adaptability or versatility
If multiple deep learning modules with different loss functions are used for video compression, then the compression can be performed with modular flexibility, but the computational efficiency decreases due to redundant calculations
Solution Approach 1:
The patent combines multiple separate deep learning modules into a single end-to-end model, eliminating redundant calculations across module boundaries. The unified architecture shares common features and computations throughout the compression pipeline, significantly improving compression efficiency while maintaining the same functional capabilities.
3Manufacturing precision
If a single loss function is used in an end-to-end model for video compression, then the loss values are controlled and video quality is improved, but the device complexity increases
Solution Approach 1:
The patent changes the optimization parameter from multiple separate loss functions to a single unified loss function that encompasses all compression objectives. This parameter change simplifies the training process and model architecture while achieving better video quality control, as the single loss function coordinates all compression stages through unified gradient descent.
4Productivity
If a single loss function is used in an end-to-end model for video compression, then the compression efficiency is improved, but the ease of operation decreases due to training complexity
Solution Approach 1:
The patent implements a unified feedback mechanism through the single loss function that provides consistent gradient signals throughout the entire end-to-end model. This feedback structure simplifies training compared to coordinating multiple separate modules, as the loss function directly guides optimization of all compression stages simultaneously, improving both training ease and compression efficiency.
Data Source
AI summary
A method for video compression processing, an electronic device, and a storage medium. In the method for video compression processing, video compression is performed by using an end-to-end model trained with a single loss function, to obtain video compression information.