Relative Quality Score for Video Transcoding Storage
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Solution Overview
Problem
Existing methods for determining whether to store high-quality video transcoded versions are inefficient, as they rely on static characteristics and often fail to identify videos that would benefit from higher quality encoding, leading to unnecessary increased data usage.
Innovation Solution
A relative video encoding quality score is calculated by comparing the quality of high and low-quality transcoded versions, taking into account subjective, perceptual quality metrics and adjusting based on the source video's quality, to determine if the high-quality version should be stored.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If higher-quality video codec is used, then video quality is improved, but data storage requirements increase
Solution Approach 1:
The patent changes the parameter of video quality by using different quality scores (e.g., VQM, PSNR) to evaluate and compare video versions, enabling dynamic selection between quality levels based on measurable parameters rather than static decisions
Solution Approach 2:
The system dynamically adjusts video quality storage decisions based on calculated quality scores and comparisons, rather than using fixed quality levels for all videos. The quality score threshold mechanism allows the system to adaptively determine when high-quality storage is justified
2Manufacturing precision
If high-quality transcoded version is stored for all videos, then visual quality is improved, but storage efficiency decreases
Solution Approach 1:
The patent applies local quality by storing high-quality versions only for specific videos that meet quality score thresholds, rather than uniformly applying high quality to all videos. This selective approach optimizes storage efficiency while maintaining quality where beneficial
Solution Approach 2:
The system performs partial action by storing high-quality versions for only a subset of videos (those exceeding quality thresholds) rather than all videos, achieving sufficient quality improvement without the excessive storage cost of universal high-quality storage
3Device complexity
If static characteristics are used for selection criteria, then processing complexity is reduced, but accuracy of quality improvement identification decreases
Solution Approach 1:
The patent replaces simple static characteristic checks with automated quality score calculation and comparison mechanisms. This substitution uses computational algorithms (VQM, PSNR) to objectively measure quality improvements, replacing subjective or simplistic selection criteria with precise measurement systems
Data Source
AI summary
A relative quality score is provided that takes into account properties of an encoded version of a source video. For example, one such quality score calculates a difference of higher and lower quality transcoded versions of the source video, and computes quality metrics for each to evaluate how similar the transcoded versions are to the source video. A relative quality score quantifying the quality improvement of the high-quality version over the low-quality version is computed. The relative quality score is adjusted based on a measurement of the quality of the source video. If the relative quality score for the video indicates a sufficient quality improvement of the high-quality version over the low-quality version, various actions are taken, such as retaining the high-quality version, and making the high-quality version available to users, e.g. via a video viewing user interface.