Live Streaming Encoding With Predicted CRF for Low-Latency Bitrate Control
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Solution Overview
Problem
Existing two-pass encoding schemes are not suitable for live streaming due to the added latency of the first-pass analysis, leading to inefficiencies in bitrate allocation and compression efficiency.
Innovation Solution
An efficient two-pass encoding scheme that extracts low-complexity features using a neural network to predict an optimized constant rate factor (CRF) for each video segment, enabling encoding at a target bitrate with reduced latency and improved compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional two-pass encoding is used to improve compression efficiency and bitrate allocation, then encoding quality is improved, but encoding latency increases due to the first-pass analysis
Solution Approach 1:
The patent extracts only the essential low-complexity features (DCT energy, temporal differences) needed for bitrate allocation, removing the need for full first-pass analysis. This selective extraction maintains compression efficiency while dramatically reducing encoding latency for live streaming.
Solution Approach 2:
The patent performs preliminary feature extraction and CRF prediction using lightweight computations before the main encoding pass. By pre-calculating only the necessary complexity metrics rather than full video analysis, it prepares encoding parameters in advance without the latency of conventional two-pass analysis.
2Productivity
If constant rate factor (CRF) encoding is used to simplify the encoding process, then encoding speed is improved, but bitrate allocation efficiency deteriorates
Solution Approach 1:
The patent applies local quality by computing DCT energy and temporal complexity metrics for each video segment individually. This allows the CRF to be locally optimized for each segment's actual complexity rather than using a global constant, improving bitrate allocation efficiency while maintaining fast encoding through the use of simple local metrics.
Solution Approach 2:
The patent dynamically changes the CRF parameter based on predicted segment complexity derived from low-computation features. Instead of using a fixed CRF value, the system adjusts the CRF parameter for each segment according to its measured DCT energy and temporal characteristics, achieving efficient bitrate allocation with minimal computational overhead.
Data Source
AI summary
Techniques for efficient two-pass encoding for live streaming are described herein. A method for efficient two-pass encoding may include extracting low-complexity features of a video segment, predicting an optimized constant rate factor (CRF) for the video segment using the low-complexity features, and encoding the video segment with the optimized CRF at a target bitrate. A system for efficient two-pass encoding may include a feature extraction module configured to extract low-complexity features from a video segment, a neural network configured to predict an optimized CRF as a function of the low-complexity features and a target bitrate, and an encoder configured to encode the video segment using the optimized CRF at the target bitrate.