Conditioned Distortion Removal Model for Multi-Level Quality
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Solution Overview
Problem
Existing deep learning approaches for distortion removal in lossy image and video codecs are limited by their fixation on a single distortion quality level, making them non-scalable and requiring retraining for different codecs and quality levels, which is not viable in lossy video coding where quality levels change frame to frame.
Innovation Solution
A system and method that trains a conditioned distortion removal model using multiple quality levels, allowing it to correct target content with varying distortion quality levels by generating a conditioned model from an initial model trained on original and reconstructed content with different quality levels, using convolutional neural networks and user-defined output branches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deep learning model is trained on a single distortion quality level, then it achieves high accuracy for that specific quality level, but it cannot handle other quality levels and requires retraining for each new quality level
Solution Approach 1:
The distortion removal model is designed with multiple output branches, each corresponding to a different distortion quality level. This allows a single model to handle multiple quality levels simultaneously, making it universal rather than specialized for one quality level only
Solution Approach 2:
The model architecture is segmented into multiple independent output branches, where each branch is responsible for removing distortions at a specific quality level. This segmentation allows the model to specialize in different quality levels while being part of a unified system
2Measurement precision
If multiple distortion removal models are trained for different quality levels, then each model achieves high accuracy for its specific quality level, but the system complexity and training time increase significantly
Solution Approach 1:
Multiple distortion removal models for different quality levels are merged into a single unified model with multiple output branches. This combines the functionality of multiple separate models into one system, reducing overall complexity while maintaining the ability to handle different quality levels
Solution Approach 2:
A single universal model performs the function of multiple specialized models by using conditional branching based on the input distortion quality level, eliminating the need to maintain and train separate models for each quality level
3Adaptability or versatility
If a distortion removal model is retrained for each new quality level, then it adapts to the new quality level, but the training time and computational resources are consumed repeatedly
Solution Approach 1:
The model is pre-configured with multiple output branches during initial training, covering various distortion quality levels. This preliminary setup eliminates the need for subsequent retraining when new quality levels need to be handled
Solution Approach 2:
The model is designed to be universally applicable across multiple quality levels from the outset, eliminating the need for repeated retraining cycles and saving significant computational time and resources
4Ease of manufacture
If the model architecture is simplified to reduce training complexity, then training becomes faster and easier, but the model's ability to handle multiple quality levels is compromised
Solution Approach 1:
The model architecture is segmented into modular output branches, where each branch can be independently trained and optimized. This modular structure makes the complex multi-quality-level model easier to train by breaking it down into manageable segments
Data Source
AI summary
Systems and methods for distortion removal at multiple quality levels are disclosed. In one embodiment, a method may include receiving training content. The training content may include original content, reconstructed content, and training distortion quality levels corresponding to the reconstructed content. The reconstructed content may be derived from distorted original content. The method may also include training distortion quality levels corresponding to the reconstructed content. The method may further include receiving an initial distortion removal model. The method may include generating a conditioned distortion removal model by training the initial distortion removal model using the training content. The method may further include storing the conditioned distortion removal model.