Aspect Ratio Error Recognition in Video Frames
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Solution Overview
Problem
Existing methods for identifying aspect ratio errors in video frames are manual and time-consuming, prone to human error, and often produce false positives when using automated tools.
Innovation Solution
A computer-implemented method that determines the positions of the top, bottom, left, and right edges of a content region within each image frame by skipping completely black rows, and generates a time series from these positions to detect aspect ratio changes based on the first differential of the time series.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of aspect ratio errors is performed, then accuracy in detecting errors is improved, but time consumption and productivity deteriorate
Solution Approach 1:
The patent replaces manual visual inspection with an automated computer-implemented method that uses image processing algorithms to detect aspect ratio errors. The system automatically identifies black bars, determines content region boundaries, and calculates aspect ratios without human intervention, thereby maintaining detection accuracy while dramatically improving processing speed and productivity.
Solution Approach 2:
The system performs self-service by automatically detecting and reporting aspect ratio errors without requiring manual review. The automated method independently identifies content regions, calculates aspect ratios, compares them against expected values, and generates error reports, eliminating the need for human operators to perform repetitive inspection tasks.
2Productivity
If traditional automated tools are used to check aspect ratio errors, then productivity is improved, but measurement precision deteriorates due to false positives
Solution Approach 1:
The patent segments the image frame into distinct regions: black bar areas and content regions. By specifically identifying and excluding black bars from the content region analysis, the system avoids misinterpreting black areas as content, thereby reducing false positives while maintaining automated processing efficiency.
Solution Approach 2:
The system applies different processing rules to different parts of the image frame. Black bar regions are identified and excluded from aspect ratio calculations, while only the content regions are analyzed. This localized approach ensures accurate detection by treating different areas of the frame according to their specific characteristics.
3Adaptability or versatility
If black bars are added to transfer video between aspect ratios, then adaptability is improved, but aspect ratio errors are introduced
Solution Approach 1:
The system performs preliminary identification and exclusion of black bars before calculating the aspect ratio of the content region. By removing the influence of black bars from the calculation in advance, the system prevents aspect ratio errors from propagating through the video processing pipeline, ensuring reliable aspect ratio measurement despite the presence of black bars for format adaptation.
Data Source
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AI summary
Techniques are disclosed for recognizing aspect ratio errors in image frames of a video and reporting the same. An aspect ratio checker application receives a video that includes multiple image frames and identifies aspect ratio changes in those image frames using a first differential of a time series that includes determined positions of the top, bottom, left, and right of content regions within the image frames. In particular, the aspect ratio checker may identify aspect ratio changes based on non-zero points of the first differential, and the aspect ratio checker further determines aspect ratios of content regions within image frames corresponding to the non-zero points of the first differential. In addition, the aspect ratio checker may generate and display a report indicating the determined aspect ratios and image frame ranges associated with those aspect ratios.