Video Processing Frame Selection for Training Efficiency
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Solution Overview
Problem
Current video super-resolution technologies face challenges in improving the quality and efficiency of video reconstruction due to the need for large computational resources and long training times, as well as imbalanced learning caused by redundant content, which leads to insufficient learning of long-tail data and poor performance in reconstructing rare features.
Innovation Solution
A method that selects relevant frames using feature selection functions to generate a reduced set of training frames, preserving important features that appear less frequently, thereby improving training quality and reducing computational resources and training time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all video frames are used for training, then training completeness is improved, but training time and computational resources increase significantly
Solution Approach 1:
The patent segments the video content by identifying and separating different type segments (e.g., static scenes, moving objects, text regions). Only frames containing specific segment types relevant to the training objective are selected, thereby reducing the total number of training frames while maintaining training completeness for target features.
Solution Approach 2:
The patent applies local quality by treating different frame types differently - frames with important features (e.g., moving objects, text) are prioritized for training, while frames with redundant content are excluded. This selective approach ensures high-quality training data without processing all frames uniformly.
2Reliability
If all video frames are used for training, then feature coverage is improved, but learning efficiency deteriorates due to redundant content
Solution Approach 1:
The patent extracts and removes redundant frame content from the training set by identifying frames with duplicate or non-essential features. This extraction process eliminates unnecessary training data while preserving frames that contain important features, thereby improving learning efficiency without sacrificing feature coverage.
Solution Approach 2:
The patent applies partial action by selecting only the necessary portion of video frames for training - specifically frames containing target features - rather than processing all frames. This selective sampling achieves sufficient feature coverage with reduced computational effort.
3Reliability
If comprehensive video content is used for training, then model generalization is improved, but training resource consumption increases
Solution Approach 1:
The patent performs preliminary action by pre-processing the video to identify and categorize frames containing different feature types before training begins. This preliminary segmentation allows the training process to focus only on relevant frames, reducing resource consumption while maintaining model generalization capability through comprehensive coverage of important feature types.
Data Source
AI summary
Embodiments of the disclosure include a method, a device, and a computer program product for video processing. This method includes: selecting frames having features of a first type from a first instance of a video as a first candidate set, the first instance having a first resolution; generating a set of training frames based at least on the first candidate set; acquiring a set of corresponding frames for the set of training frames in a second instance of the video, the second instance having a second resolution lower than the first resolution; and determining, using the set of training frames and the set of corresponding frames, a conversion parameter for conversion from the second resolution to a third resolution. This solution provides a smaller-scale and higher-quality training set for the training of a video conversion model, thus improving the quality of training while saving computational resources and increasing training speed.