Video Frame Orientation Adjustment for Quality Metrics
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Solution Overview
Problem
Online videos often suffer from obstructions, glare, and poor framing, leading to decreased video quality due to issues like objects being hidden or moved out of frame, which affects the viewer's experience and requires improvement in multimedia quality evaluation and remediation.
Innovation Solution
A computer-implemented method that generates dataframes for video frames and reference frames, compares them to determine quality metrics, and alters the frame orientation or captures settings to improve video quality by addressing obstructions and glare, using machine learning models and viewer preferences to enhance the viewing experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the video frame is displayed in the original orientation, then the capture process is simple, but the quality metric decreases due to obstructions and poor framing
Solution Approach 1:
The patent applies inversion by rotating video frames 180 degrees or reflecting them horizontally/vertically to correct poor framing and orientation issues. This transforms incorrectly oriented frames into properly oriented ones, directly improving video quality without requiring complex re-capture procedures.
Solution Approach 2:
The system dynamically adjusts frame orientation based on real-time quality metrics. Instead of using a fixed display orientation, the patent determines the optimal orientation through quality assessment and applies transformations (rotation or reflection) to maximize video quality, making the display adaptive rather than static.
2Manufacturing precision
If the frame orientation is altered to improve quality, then the quality metric increases, but the processing time increases
Solution Approach 1:
The patent performs preliminary quality assessment on video frames before final display or processing. By evaluating orientation quality metrics in advance and determining the optimal orientation beforehand, the system avoids iterative adjustments and reduces overall processing time while ensuring quality improvement.
Solution Approach 2:
The system changes the orientation parameter (rotation angle or reflection axis) based on quality metric evaluation. By systematically testing different orientation parameters and selecting the one that maximizes quality, the patent achieves quality improvement with minimal processing overhead through parameter optimization rather than exhaustive transformation.
3Measurement precision
If machine learning models are used to determine quality metrics, then the accuracy of quality assessment improves, but the computational complexity increases
Solution Approach 1:
The patent introduces an intermediary quality assessment layer between raw video frames and final processing decisions. The machine learning model acts as a mediator that evaluates orientation quality and provides guidance for frame transformation, enabling accurate quality assessment without requiring complex manual analysis or trial-and-error processing.
Solution Approach 2:
The system replaces manual or rule-based quality assessment mechanisms with machine learning models. This substitution enables more accurate and nuanced quality evaluation of video frames, automatically detecting orientation issues and optimal transformations without relying on simple threshold-based or heuristic methods.
Data Source
AI summary
A computer-implemented method, a computer system and a computer program product improve the quality of multimedia. The method includes displaying a current frame of a video. The method also includes generating dataframes for the current frame and for a reference frame of the video. The method further includes comparing the dataframes for the reference and current frames. In addition, the method includes determining a quality metric of the current frame based on the comparison of the dataframes for the reference and current frames. Finally, the method includes altering an orientation of the display of the current frame in response to determining that the quality metric of the current frame is below a threshold.