Aspect Ratio Validation via Face Detection
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Solution Overview
Problem
Digital video content often arrives with incorrect or missing metadata regarding aspect ratios, leading to improper encoding and a degraded viewing experience, as service providers lack efficient automated methods to verify the correct aspect ratio without manual human intervention.
Innovation Solution
The technology employs computer pattern recognition to detect faces in videos, comparing them to candidate aspect ratio models to determine if the video is correctly encoded, flagging issues for potential re-encoding or human review, and using machine learning to create models for identifying stretched, squeezed, or normal faces based on predefined ranges and relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual human inspection is used to verify aspect ratio, then measurement precision is improved, but productivity deteriorates due to time-consuming manual review
Solution Approach 1:
The system enables self-service by having the video content itself provide the verification through automated object detection and aspect ratio analysis. The system detects objects in the video, measures their dimensions, and automatically determines whether the aspect ratio is correct without requiring human intervention for each verification step.
Solution Approach 2:
The patent replaces the mechanical human inspection process with an automated computer-based system that uses image processing algorithms, object detection models, and computational geometry to verify aspect ratios. This substitution eliminates manual labor while maintaining verification accuracy through algorithmic analysis of visual content.
2Productivity
If automated detection methods are implemented, then productivity is improved, but measurement precision may deteriorate due to algorithmic limitations
Solution Approach 1:
The system incorporates feedback mechanisms where the detected object dimensions and calculated aspect ratios are continuously validated against expected values. The system can iteratively refine its detection results by comparing automated measurements against known parameters or by seeking confirmation through multiple detection passes, thereby maintaining high precision while scaling for automated processing.
Solution Approach 2:
The verification process is segmented into distinct operational steps: object detection, dimension measurement, aspect ratio calculation, and validation. This segmentation allows each step to be optimized independently, with specialized algorithms for detection and separate validation logic, thereby maintaining overall precision while enabling automated throughput.
3Measurement precision
If comprehensive object detection is performed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system extracts only the necessary information for aspect ratio verification by detecting and measuring specific objects whose dimensions are relevant to the video's aspect ratio. Rather than analyzing every pixel or object in the video, the system selectively identifies key objects (such as characters, props, or scene elements) and measures their dimensions to determine aspect ratio correctness, thereby reducing computational complexity while maintaining verification precision.
Data Source
AI summary
A technology for aspect ratio validation is provided. An object may be detected in a visual media presentation. The detected object may be compared to a first candidate aspect ratio object of a same class of objects as the object. The detected object may also be compared to a second candidate aspect ratio object, which may also be of a same class of objects as the detected object. A determination may be made as to which of the first and second candidate aspect ratio objects the object corresponds.