Aggregated Quality Score for Media Transcoding Optimization
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Solution Overview
Problem
Existing metrics for evaluating media content quality are inadequate in quantifying subjective qualities and fail to effectively characterize poor quality, leading to challenges in automating quality assessment and optimizing media encoding in large-scale media processing infrastructure.
Innovation Solution
A method and system that determine an aggregated quality score for transcoded media content by mapping degradation metric values to calibrated scores using an exponential weighting function, allowing for selective reencoding based on the quality score to improve or reduce quality, thereby optimizing bitrate and storage usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing quality metrics are used to evaluate media content, then some aspect of quality can be measured, but the metrics fail to robustly characterize subjective quality properties and do not work well for poor quality content
Solution Approach 1:
The patent segments quality assessment into multiple independent degradation metric types (e.g., compression artifacts, noise, distortion) rather than relying on a single aggregate metric. Each degradation metric evaluates a specific aspect of quality degradation, allowing the system to precisely characterize different types of quality issues independently and then combine them for comprehensive assessment.
Solution Approach 2:
The patent introduces a new dimensional approach by mapping degradation metric values to calibrated scores through exponential weighting functions. This transforms the assessment from traditional linear metric evaluation to a multi-dimensional scoring system that better captures subjective quality perception, particularly for poor quality content where traditional metrics fail.
2Adaptability or versatility
If multiple degradation metrics are used to cover different aspects of quality, then coverage of quality domain improves, but the complexity of processing and aggregating multiple metrics increases
Solution Approach 1:
The patent performs preliminary calibration of degradation metric values by mapping them to standardized score ranges before aggregation. This preliminary action normalizes different metric types onto a common scale, simplifying the subsequent aggregation process and reducing the complexity of combining multiple metrics while maintaining comprehensive coverage.
Solution Approach 2:
The patent applies exponential weighting functions that dynamically adjust the contribution of each degradation metric based on its calibrated score. This parameter change approach allows the system to adaptively emphasize or de-emphasize specific degradation types during aggregation, managing processing complexity while maintaining versatile quality domain coverage.
3Measurement precision
If traditional quality metrics focus on modeling what makes content good, then high quality content can be characterized well, but poor quality content cannot be effectively characterized
Solution Approach 1:
The patent inverts the traditional approach by focusing on modeling degradation and poor quality characteristics rather than modeling what makes content good. By measuring and characterizing degradation metrics (artifacts, noise, distortion) and their calibration to scores, the system effectively captures poor quality states while maintaining the ability to assess the full quality range through the scoring mechanism.
Data Source
AI summary
Systems and methods for transcoding media content are disclosed. In some embodiments, the method includes obtaining a transcoded media content that is transcoded from an uploaded media content. The method includes determining a plurality of degradation metric values corresponding to the transcoded media content based on the uploaded media content and the transcoded media content, each degradation metric value corresponding to a different degradation metric type. The method includes mapping each degradation metric value to a respective calibrated score to obtain a plurality of calibrated scores. The method includes determining an aggregated quality score of the transcoded media content based on the plurality of calibrated scores and an exponential weighting function. The exponential weighting function exponentiates each of the calibrated scores by a respective weighting exponent and aggregates the exponentiated calibrated scores. The method includes selectively reencoding the transcoded media content based on the aggregated quality score.