ML Video Quality Prediction for Publisher Feedback
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Solution Overview
Problem
Video publishers face uncertainty about the popularity of their content before publication, leading to the proliferation of uninteresting content on social networking systems, as conventional approaches lack effective guidance for creating content that will engage audiences.
Innovation Solution
A machine learning model is trained using labeled training videos to evaluate video quality based on objectives like viewer retention time, allowing publishers to analyze their videos and improve them before publication, using a multi-stage model comprising a deep neural network and a sparse neural network to assign videos to quality categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If video publishers post content without evaluation, then publication speed is improved, but content quality deteriorates leading to uninteresting content proliferation
Solution Approach 1:
The system performs preliminary evaluation of video content using machine learning models before publication. The automated quality assessment predicts viewer retention metrics and generates feedback reports in advance, allowing publishers to improve content before posting without delaying publication significantly.
Solution Approach 2:
The patent replaces manual content review processes with automated machine learning-based evaluation systems. The ML models automatically analyze video content, predict quality metrics, and generate feedback without human intervention, maintaining fast publication speeds while improving content quality assessment.
2Measurement precision
If automated evaluation systems are implemented, then content quality assessment is improved, but system complexity increases
Solution Approach 1:
The system introduces trained machine learning models as intermediaries between video content and quality assessment. These pre-trained models serve as mediators that automatically evaluate content based on learned patterns from training data, providing accurate quality predictions without requiring complex real-time analysis infrastructure.
Solution Approach 2:
The patent transforms complex video quality assessment into predictions of specific measurable parameters such as viewer retention time and engagement metrics. By focusing on these key parameters rather than comprehensive quality analysis, the system achieves accurate assessment with reduced computational complexity.
3Measurement precision
If comprehensive video analysis is performed, then evaluation accuracy is improved, but processing time increases
Solution Approach 1:
The system extracts and analyzes only the most relevant features from video content for quality prediction, rather than performing comprehensive analysis of all video attributes. The ML models focus on key indicators such as visual engagement patterns and audio characteristics that most strongly correlate with viewer retention, reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent implements partial analysis by evaluating only the portions of video content that most significantly impact quality metrics. The system analyzes representative segments and uses ML models to generalize findings to the entire video, achieving accurate evaluation without processing every frame or second of content.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can collect a set of training videos as training data, wherein the set of training videos are labeled with one or more labels based on one or more video quality metrics associated with an evaluation objective. A machine learning model is trained based on the training data. A video to be evaluated is received. The video is assigned to a first video quality category of a plurality of video quality categories based on the machine learning model.