Video Transcoding Rate Control Using Rate-Distortion Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional transcoding approaches rely on fixed rate control thresholds, which are insufficient for maintaining uniform quality and efficiency across diverse video content, leading to inefficiencies and non-optimal bitrate allocation in large-scale video hosting platforms.
Innovation Solution
A learning model is trained to predict rate-distortion behavior based on video content complexity, allowing for optimal bitrate allocation and transcoding parameters to be selected for each video chunk, minimizing average bitrate while maintaining aggregate quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed rate control thresholds are used for transcoding, then the transcoding process is simple and fast, but the video quality is non-uniform and bitrate allocation is non-optimal across diverse video content
Solution Approach 1:
The patent implements dynamic rate control by using a learning model to predict rate-distortion behavior and select transcoding parameters adaptively for each video chunk based on its complexity features, replacing static fixed thresholds with dynamic, content-aware parameter selection that optimizes both quality uniformity and transcoding efficiency
Solution Approach 2:
The patent changes the transcoding parameters (bitrate, quality settings) based on predicted rate-distortion clusters derived from video complexity features, allowing the system to adjust parameters dynamically according to content characteristics rather than using fixed thresholds, thereby achieving optimal bitrate allocation and uniform quality across diverse content
2Productivity
If fixed rate control thresholds are used for transcoding, then the transcoding process is simple and fast, but the bitrate allocation is non-optimal leading to increased storage and egress costs
Solution Approach 1:
The patent optimizes bitrate allocation by changing transcoding parameters based on predicted rate-distortion clusters, selecting optimal bitrate values for each video chunk according to its complexity features, thereby minimizing total storage and egress requirements while maintaining uniform quality across the video corpus
Solution Approach 2:
The patent replaces the mechanical, rule-based fixed threshold system with a learning model that predicts rate-distortion behavior, enabling intelligent, data-driven bitrate allocation that reduces overall storage and transmission costs while maintaining quality standards
Data Source
AI summary
A learning model is trained for rate-distortion behavior prediction against a corpus of a video hosting platform and used to determine optimal bitrate allocations for video data given video content complexity across the corpus of the video hosting platform. Complexity features of the video data are processed using the learning model to determine a rate-distortion cluster prediction for the video data, and transcoding parameters for transcoding the video data are selected based on that prediction. The rate-distortion clusters are modeled during the training of the learning model, such as based on rate-distortion curves of video data of the corpus of the video hosting platform and based on classifications of such video data. This approach minimizes total corpus egress and/or storage while further maintaining uniformity in the delivered quality of videos by the video hosting platform.