Scene-Specific Video Reconstruction Models
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Solution Overview
Problem
Conventional video reconstruction models face challenges in improving quality and efficiency due to the complexity of training with large amounts of video data, requiring significant computational resources and often resulting in overfitting or failing to effectively learn details, especially for videos with multiple complex scenes.
Innovation Solution
The approach involves dividing a video into frame groups based on different scenes, training separate models for each scene, which allows for better adaptation and learning of specific scene features, reducing computational resources needed and improving training speed and reconstruction quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a single model is used to process all video frames, then device complexity is reduced, but manufacturing precision deteriorates because the model cannot effectively learn specific scene features
Solution Approach 1:
The patent divides the video into multiple frame groups based on scene changes, and trains a separate model for each frame group. This segmentation allows each model to specialize in specific scene features, improving reconstruction quality while managing complexity through modular design.
Solution Approach 2:
Different models are trained for different frame groups corresponding to different scenes. Each model develops specialized features for its specific scene type, achieving local optimization of reconstruction quality for each scene rather than a one-size-fits-all approach.
2Adaptability or versatility
If large amounts of video data are used for training, then adaptability improves, but loss of time increases due to significant computational resources required
Solution Approach 1:
The training process is segmented by dividing video data into frame groups based on scenes. Each model is trained on a subset of data corresponding to specific scenes, reducing the computational burden and training time for each individual model while maintaining overall adaptability across diverse scenes.
Solution Approach 2:
Instead of training a single model on all video data, the patent applies partial action by training multiple models on different subsets of data. This approach achieves comprehensive scene coverage through multiple specialized models rather than one comprehensive model, reducing the training time for each model while maintaining overall adaptability.
3Productivity
If a single model processes all frames, then device complexity is reduced, but productivity deteriorates due to slower training speed
Solution Approach 1:
The video processing task is segmented into multiple frame groups, each processed by a dedicated model. This allows parallel training of multiple smaller models, which increases training speed compared to training one large model, while the modular structure manages device complexity through organized separation of functions.
Solution Approach 2:
The patent uses multiple models for different frame groups rather than a single comprehensive model. This partial action approach distributes the computational workload across multiple smaller models, improving training speed through parallel processing while managing complexity through functional decomposition.
Data Source
AI summary
Embodiments of the present disclosure relate to a computer-implemented method, a device, and a computer program product. A method includes: determining a first group of frames corresponding to a first scene and a second group of frames corresponding to a second scene different from the first scene in a reference video, respectively. The first group of frames and the second group of frames each have a first resolution. The method further includes determining a first model for the first scene and a second model for the second scene, respectively. The first model and the second model are respectively used to convert frames corresponding to the first scene and the second scene from the first resolution to a second resolution different from the first resolution. The method further includes training the first model and the second model using the first group of frames and the second group of frames, respectively.