ML Content Type Detection for Video Format Mismatch Screening
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Solution Overview
Problem
Motion picture and video-based content production companies face challenges in detecting mismatches between expected and actual video formats due to varying workflows and creative processes, leading to potential defects in content that may go undetected until later stages, causing consumer issues and additional costs.
Innovation Solution
An automated machine learning (ML) model-based system is employed to distinguish between different content types by analyzing video format properties such as EOTF, quantization range, and color encoding primaries, using independent variables to predict content type accurately and reduce human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated image analysis is implemented to detect content format mismatches, then detection precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces an automated image analysis system as an intermediary between content ingestion and distribution. This system includes a trained machine learning model that analyzes video frames to detect format mismatches, acting as a mediator that automatically identifies issues without requiring manual review while maintaining high detection precision across multiple content formats and workflows
Solution Approach 2:
The system performs preliminary detection of content format mismatches during the content ingestion and processing phase, before distribution to consumers. By training the machine learning model on diverse content samples and applying it early in the workflow, the system identifies format issues proactively, preventing defective content from reaching consumers and avoiding later correction costs
2Measurement precision
If manual quality control is performed by specialists, then detection capability is improved, but productivity decreases
Solution Approach 1:
The patent implements a self-service quality control system where the machine learning model automatically analyzes content format compliance without requiring specialized human expertise. The system trains on diverse content samples and independently detects mismatches, eliminating the need for specialized specialists while maintaining high detection capability and significantly improving processing throughput and productivity
Solution Approach 2:
The system replaces the mechanical process of manual quality control by specialists with an automated machine learning-based image analysis system. This substitution eliminates human labor requirements while maintaining or improving detection capability through consistent application of trained models, thereby dramatically increasing productivity and reducing operational costs
3Adaptability or versatility
If content is processed through multiple workflows from different sources, then adaptability is improved, but reliability decreases
Solution Approach 1:
The patent applies local quality analysis by examining specific regions and characteristics of video frames to detect format mismatches. The machine learning model analyzes local features such as color encoding, quantization ranges, and EOTF characteristics in different parts of the content, enabling reliable detection across diverse workflows and sources while maintaining adaptability to various content types and formats
Data Source
AI summary
A system includes a hardware processor, and a memory storing a software code and at least one machine learning (ML) model trained to distinguish between a plurality of content types. The hardware processor executes the software code to receive a content file including data identifying a dataset contained by the content file as being a first content type of the plurality of content types; predict, using the at least one ML model and the dataset, based on at least one image parameter, a first probability that a content type of the dataset matches the first content type identified by the data; and determine, based on the first probability, that the content type of the dataset (i) is the first content type identified by the data, (ii) is not the first 10 content type identified by the data, or (iii) is of an indeterminate content type.