Video Jitter Detection Model for Robust Shot Switching Analysis
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Solution Overview
Problem
Existing video jitter detection methods based on image processing technologies suffer from complex calculations, low accuracy, and poor robustness, particularly in scenarios like shot switching and video special effects.
Innovation Solution
A video jitter detection method utilizing a detection model trained on a large number of video samples, where the model is refined through subsets with labeled and unlabeled data, and further corrected using manual evaluation adjustments to improve accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing technology is used for video jitter detection, then the detection can be performed on video content, but the calculation becomes complex and accuracy decreases
Solution Approach 1:
The patent replaces traditional image processing mechanical systems (optical flow, feature point matching, gray distribution analysis) with a deep learning-based detection model. This substitution allows the system to automatically learn optimal jitter detection features from video data, achieving higher accuracy while the model handles calculation complexity internally through optimized neural network operations.
Solution Approach 2:
The patent uses a detection model that has been trained on a large number of video samples (copied learning patterns from training data) to evaluate new videos. Instead of performing complex real-time image processing calculations, the system copies the learned detection patterns from the training phase and applies them to new videos, significantly reducing calculation complexity while maintaining high accuracy.
2Reliability
If traditional image processing methods are used, then jitter detection can be implemented, but robustness is poor in scenarios like shot switching and special effects
Solution Approach 1:
The detection model is designed with multi-functionality to handle various video scenarios including normal video, shot switching, and special effects. The model learns diverse patterns during training from multiple types of video data, enabling it to adaptively detect jitter across different video conditions without requiring separate processing methods for each scenario.
Solution Approach 2:
The patent performs preliminary action by training the detection model on a large number of diverse video samples before actual jitter detection. This pre-training process enables the model to learn robust features and patterns that generalize well to various video scenarios, including edge cases like shot switching and special effects, improving reliability without compromising adaptability.
3Measurement precision
If a detection model trained on large number of samples is used, then detection accuracy and robustness are improved, but training and model refinement become complex
Solution Approach 1:
The patent segments the training process into distinct phases: initial model training on a large video sample set, evaluation on a test set, and iterative refinement using feedback from detection results. This segmentation allows the complex training process to be managed in manageable stages, improving accuracy while controlling training complexity through structured progression.
Solution Approach 2:
The patent implements feedback mechanisms where detection results from the test set are used to evaluate model performance and guide further training adjustments. The feedback loop allows iterative improvement of the detection model, with each training cycle informed by actual detection outcomes, thereby systematically improving accuracy while managing training complexity through data-driven optimization.
Data Source
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AI summary
The present invention provides a video jitter detection method and an apparatus. The video jitter detection method includes: acquiring a video; inputting the video into a detection model to obtain an evaluation value of the video, where the evaluation value is used to indicate a degree of jitter of the video; where the detection model is a model obtained by training using video samples in a video sample set as inputs and evaluation values of the video samples in the video sample set as outputs. By inputting the video to be detected into the detection model, the evaluation value of the video can be acquired through the detection model, thereby whether the video is jittery is determined, which realizes the video jitter detection end-to-end, and improves the detection accuracy and robustness of video jitter.