Content Classifiers for Preplay TV Picture and Sound Optimization
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Solution Overview
Problem
Existing methods for automatically adjusting TV picture and sound settings during playback require sampling, which can disrupt the viewing experience and are inadequate for optimizing multiple parameters simultaneously.
Innovation Solution
Implementing a system that modifies metadata with optimized audio and display settings before playback, using machine learning models trained by crowdsourced data or media content providers to predict settings based on streaming parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If sampling playback is used to determine picture and sound corrections, then automatic content recognition can be achieved, but the viewing experience is disrupted and corrections cannot be applied invasively
Solution Approach 1:
The system performs picture and sound corrections before playback rather than during playback. Metadata containing correction parameters is prepared in advance, allowing the viewing experience to remain uninterrupted while still achieving automatic content recognition and optimization.
2Manufacturing precision
If multiple parameters are adjusted simultaneously to optimize picture and sound, then comprehensive optimization is achieved, but the complexity of finding optimal combinations increases
Solution Approach 1:
The system segments the optimization process by handling picture and sound parameters separately through distinct metadata files (e.g., PQ metadata and AQ metadata). This allows comprehensive optimization of multiple parameters while reducing the complexity of finding optimal combinations by treating different parameter sets independently.
Solution Approach 2:
The system changes parameters in advance by preparing correction metadata before playback. This includes modifying picture quality parameters (brightness, contrast, color) and audio quality parameters (volume, bass, treble) based on content analysis, allowing comprehensive optimization without real-time complexity.
3Speed
If corrections are applied during playback, then real-time adjustment is achieved, but the corrections become invasive and jarring to viewers
Solution Approach 1:
The system applies corrections in advance by preparing all necessary picture and sound adjustment metadata before playback begins. This eliminates viewing disruptions while still achieving real-time effectiveness, as the corrections are ready to be applied immediately when playback starts without causing jarring transitions.
Data Source
AI summary
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for modifying one or more parameters of a data streaming payload to add optimized display and/or audio settings as metadata. An example embodiment operates by training and operating one or more machine learning models to predict optimized picture and sound settings based on previous user prioritization of changes. Having the optimized display settings in advance allows adjustments to be made in advance of playback.