Content Fragment Selection via Perceptual Quality Metrics
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Solution Overview
Problem
Conventional adaptive streaming techniques for video and audio content rely on bit rate as a proxy for quality, leading to inefficient bandwidth allocation and variability in content quality, which negatively impacts user experience.
Innovation Solution
The use of quality metrics derived from human subject testing and machine learning to guide fragment selection in content delivery, focusing on maintaining consistent quality within available bandwidth, rather than maintaining a constant bit rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional adaptive streaming techniques use bit rate as a proxy for quality and stream the highest constant bit rate below available bandwidth, then bandwidth utilization is maximized, but content quality consistency deteriorates
Solution Approach 1:
The patent changes the parameter used for quality assessment from bit rate to perceptual quality metrics. Instead of selecting fragments based on constant or highest bit rate, the system evaluates fragments using perceptual quality measurements and selects those that maintain consistent quality levels, thereby resolving the contradiction between bandwidth utilization and quality consistency.
Solution Approach 2:
The patent replaces the mechanical/bit-rate-based selection mechanism with a perceptual quality-based selection mechanism. Rather than relying on objective bit rate metrics, the system uses perceptual quality assessments (potentially involving machine learning models trained on human subject testing) to guide fragment selection, achieving both efficient bandwidth use and consistent quality.
2Ease of manufacture
If conventional techniques allocate bandwidth based on constant bit rate, then bandwidth allocation is simplified, but quality efficiency deteriorates
Solution Approach 1:
The patent changes the control parameter from bit rate to perceptual quality metrics. This allows for more efficient quality outcomes while maintaining manageable complexity through automated quality assessment and fragment selection algorithms that evaluate perceptual quality and make optimal selection decisions.
3Quantity of substance
If conventional streaming maintains highest constant bit rate, then bandwidth consumption is maximized, but perceived quality consistency worsens
Solution Approach 1:
The patent fundamentally changes the parameter for fragment selection from bit rate to perceptual quality metrics. By measuring and selecting fragments based on their actual perceived quality rather than their bit rate, the system achieves consistent quality experience while consuming bandwidth more efficiently, avoiding over-allocation to high bit rate fragments that do not provide proportional quality improvements.
Solution Approach 2:
The patent substitutes the bit rate-based selection mechanism with a perceptual quality-based selection mechanism. This replacement enables the system to identify and select fragments that provide consistent quality perception, eliminating the inconsistency that arises from maintaining constant or highest bit rate across different content segments.
Data Source
AI summary
Techniques for delivering content are described that vary the bit rate with which the content is delivered to achieve a consistent level of quality from the user's perspective. This is achieved through the use of quality metrics associated with content fragments that guide decision making in selecting from among the different size fragments that are available for a given segment of the content. Fragment selection attempts to optimize quality within one or more constraints.