Video Transcoding Rate Control Using Predicted Rate-Distortion Clusters
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Solution Overview
Problem
Conventional transcoding methods rely on fixed rate control thresholds, which fail to account for varying video content complexity, leading to inefficient bitrate and quality targets, and are computationally infeasible for large video corpora due to high calculation costs of rate-distortion curves.
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, minimizing average bitrate while maintaining uniform quality across a large corpus of videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional transcoding methods use fixed rate control thresholds, then the transcoding process is simple to implement, but the bitrate and quality targets are inefficient and do not account for varying video content complexity
Solution Approach 1:
The patent changes the rate control parameters from fixed thresholds to dynamic values predicted by a learning model. The model predicts rate-distortion characteristics based on video content features, allowing the transcoding system to adapt bitrate and quality targets to the specific complexity of each video clip, thereby improving efficiency while maintaining operational simplicity through automated parameter adjustment
Solution Approach 2:
The learning model enables the transcoding system to self-adjust to different video content characteristics without manual intervention. The system automatically analyzes video features and selects appropriate transcoding parameters based on predicted rate-distortion behavior, making the system self-adaptive to varying content complexity while maintaining simple user interaction
2Measurement precision
If conventional methods calculate rate-distortion curves for all videos in a large corpus, then accurate transcoding parameters can be determined, but the computational cost becomes infeasible
Solution Approach 1:
Instead of calculating rate-distortion curves for every video in the corpus, the patent creates a learning model that copies and generalizes rate-distortion characteristics from a representative sample of videos. The model learns the relationship between video content features and rate-distortion behavior, then applies this knowledge to predict parameters for new videos without performing exhaustive calculations, thereby maintaining accuracy while dramatically reducing computational cost
Solution Approach 2:
The learning model is trained in advance on a corpus of videos to predict rate-distortion behavior before actual transcoding occurs. By pre-learning the relationships between video features and rate-distortion characteristics, the system can quickly predict parameters for new videos without performing time-consuming rate-distortion curve calculations at inference time, thus reducing computational burden while maintaining precision
3Reliability
If the transcoding system processes every video clip individually with high computational accuracy, then quality consistency is achieved, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent replaces the mechanical process of calculating rate-distortion curves for each video clip with a learning-based prediction system. Instead of performing computationally intensive mathematical calculations for every clip, the system uses a trained learning model to predict rate-distortion characteristics based on video features, substituting mechanical computation with intelligent prediction that maintains quality consistency while significantly reducing processing time
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.