Video Rendition Normalization for Automated Error Checking
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Solution Overview
Problem
Existing quality control (QC) processes for media content are time-consuming and inefficient when dealing with multiple renditions of the same content, particularly due to the need for manual visual review and the inability of current comparison techniques to handle differing attributes such as resolution, aspect ratio, and frame rate.
Innovation Solution
A method and system for normalizing or adjusting video and audio attributes of different renditions to match those of a reference rendition, allowing for comparative analysis using techniques like PSNR to identify errors or issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual review is performed for each rendition, then quality control accuracy is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary automated QC analysis on multiple renditions before manual review, pre-identifying errors and flagging only problematic segments for manual inspection. This preliminary filtering action reduces the scope of manual review from entire renditions to specific error-prone segments, thereby maintaining QC accuracy while significantly reducing time consumption.
Solution Approach 2:
The system creates transformed copies of renditions with normalized attributes (resolution, aspect ratio, frame rate) to enable automated comparison. By working with these copied and normalized versions rather than original diverse renditions, the system can perform efficient automated analysis that identifies errors without requiring manual review of every frame, thus reducing time while maintaining detection accuracy.
2Productivity
If automated tests are used for QC analysis, then time efficiency is improved, but detection capability for subtle errors worsens
Solution Approach 1:
The system segments the QC process into two distinct stages: automated analysis for initial error identification and manual review for verification of subtle errors. By dividing the overall QC task into these segments, the system leverages the speed of automated tests while preserving human capability for detecting subtle issues, thus achieving both time efficiency and comprehensive detection capability.
Solution Approach 2:
The system introduces an intermediary layer that bridges automated tests and manual review. This intermediary component analyzes automated test results, prioritizes segments requiring manual inspection, and guides human reviewers to focus on areas most likely containing errors. This intermediary mediation maximizes the efficiency of automated testing while ensuring subtle errors are not missed.
3Reliability
If multiple renditions are reviewed independently, then comprehensive error detection is improved, but workload and time consumption increase
Solution Approach 1:
The system merges the analysis of multiple renditions by transforming them to common attributes and performing comparative analysis. Instead of independently reviewing each rendition, the system combines them into a unified analysis framework where errors can be detected by comparing differences between renditions. This merging approach maintains comprehensive error detection while reducing total review time through shared analysis efforts.
Solution Approach 2:
The system creates equipotential conditions by normalizing all renditions to the same resolution, aspect ratio, and frame rate before comparison. This normalization ensures that all renditions are on equal footing for automated analysis, enabling direct comparison without the complexity of handling diverse attributes. The equipotential state allows efficient batch processing while maintaining comprehensive error detection across all original renditions.
4Ease of operation
If renditions with different attributes are directly compared, then analysis simplicity is improved, but comparison accuracy worsens
Solution Approach 1:
The system changes the parameters of renditions by transforming them to normalized attributes (standard resolution, aspect ratio, frame rate) before comparison. This parameter transformation maintains analysis simplicity through automated processing while ensuring comparison accuracy by eliminating attribute mismatches. The parameter changes are performed algorithmically, preserving the ease of automated operation while achieving accurate comparisons.
Data Source
AI summary
A system and method are for comparing video content with different attributes. The method includes determining for first and second renditions of video content respective first and second values for each video attribute of the first and second renditions; identifying video attributes for which the first value is different from the second value; determining scaling or adjustment parameters for matching or normalizing the first and second values; generating a transformed rendition of the first rendition having an adjusted value for the first video attribute so that the video attributes of a transformed first rendition match those of the second rendition or a transformed second rendition; and performing a comparative analysis between the transformed first rendition and the second rendition or the transformed second rendition to identify frames in which a comparative performance metric indicates an error or issue in the first rendition or the second rendition.