Encoder Parameter Selection by Media Class for Efficient Encoding
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Solution Overview
Problem
Conventional systems consume significant computing resources and suffer from increased latency and decreased efficiency due to the use of a single set of encoder parameter settings for diverse media items, failing to optimize network bandwidth and media quality.
Innovation Solution
A machine learning model trained to predict optimal encoder parameter settings based on media class characteristics, reducing resource consumption by encoding media items with tailored settings that minimize size and maximize quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single set of encoder parameter settings is used for all media items, then device complexity is reduced, but computing resource usage increases and efficiency decreases
Solution Approach 1:
The patent segments media items into different media classes based on their characteristics (e.g., video quality, resolution, content type). Each media class is assigned specific encoder parameter settings optimized for its characteristics. This segmentation allows the system to use tailored settings for each class rather than a single universal setting, improving encoding efficiency and resource utilization while maintaining manageable complexity through automated classification.
2Ease of operation
If a single set of encoder parameter settings is used for all media items, then ease of operation is improved, but computing resource usage increases
Solution Approach 1:
The system implements self-service by automatically classifying media items into appropriate media classes and selecting optimal encoder parameter settings without manual intervention. The automated classification and settings selection process eliminates the need for operators to manually configure encoder settings for each media item, maintaining ease of operation while significantly reducing computing resource usage through intelligent, adaptive parameter selection.
3Quantity of substance
If media items are encoded with tailored encoder parameter settings, then network bandwidth optimization is improved, but device complexity increases
Solution Approach 1:
The patent applies parameter changes by adjusting encoder parameter settings based on the classified media class of each media item. Different media classes (e.g., high-quality video, standard video, audio-only) receive different parameter configurations that optimize network bandwidth utilization for their specific characteristics. This approach enables efficient bandwidth management while the automated classification system keeps the complexity of parameter management tractable.
4Manufacturing precision
If media items are encoded with tailored encoder parameter settings, then media quality is improved, but device complexity increases
Solution Approach 1:
The patent implements local quality by applying different encoder parameter settings tailored to the specific characteristics of each media class. High-quality video content receives settings optimized for preserving visual fidelity, while audio-only content receives settings optimized for audio quality. This localized optimization of encoding parameters for each media class improves overall media quality while the automated classification system manages the complexity of having multiple setting configurations.
Data Source
AI summary
A media item to be provided to users of a platform is identified. The media item is associated with a media class of one or more media classes. An indication of the media item is provided as input to a machine learning model trained based on historical encoding data to predict, for a given media item, a set of encoder parameter settings that satisfy a performance criterion in view of a respective media class of the given media item. The historical encoding data includes a prior set of encoder parameter settings that satisfied the performance criterion with respect to a prior media item associated with the respective class. Encoder parameter settings that satisfy the performance criterion in view of the media class is determined based on an output of the model. The media item is caused to be encoded using the determined encoder parameter settings.