Video Encoder Bit Rate Stabilization via Quantization Adjustment
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Solution Overview
Problem
Existing video streaming technologies face challenges in maintaining video quality due to network conditions changes, leading to unnecessary bitrate reductions and quality degradation, especially when low-complexity video segments are misinterpreted as bandwidth declines.
Innovation Solution
Implementing video encoder bit rate stabilization techniques that dynamically adjust quantization parameters and Group of Pictures (GOP) structures based on frame complexity and network conditions, using techniques such as quantization parameter adjustment and GOP adjustment to maintain a stable bitrate while ensuring video quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If servers adjust bitrate to account for monitored bandwidth changes, then network adaptability is improved, but video quality deteriorates due to unnecessary bitrate reductions during low-complexity segments
Solution Approach 1:
The patent applies local quality by differentiating between low-complexity and high-complexity video segments, applying different bitrate adjustment strategies to each. Low-complexity segments maintain higher bitrates to preserve quality, while high-complexity segments receive bitrate reductions only when network conditions deteriorate. This localized approach prevents unnecessary quality degradation across the entire video stream.
Solution Approach 2:
The patent changes the parameter being monitored from raw bitrate to frame complexity metrics (such as motion vectors, quantization parameters, or perceptual quality measures). By detecting actual video content complexity rather than merely observing bitrate fluctuations, the system can distinguish between quality-preserving bitrate reductions and network-constrained reductions, enabling more intelligent adaptive bitrate adjustment.
2Loss of energy
If bitrate is reduced to compensate for network changes, then network bandwidth utilization is improved, but video quality is degraded
Solution Approach 1:
The patent implements dynamic bitrate adjustment based on real-time frame complexity analysis. Rather than applying static or uniformly dynamic bitrate reduction, the system continuously adapts the bitrate according to the actual complexity of each video segment. This allows the system to maintain high bitrate during simple segments (efficient bandwidth use) while reducing bitrate only when necessary during complex segments or network congestion.
Solution Approach 2:
The patent introduces feedback mechanisms that monitor both network conditions and video content characteristics, using this information to make informed bitrate decisions. The system feeds back frame complexity metrics and network status to the bitrate control algorithm, creating a closed-loop system that adjusts bitrate based on actual needs rather than predetermined rules, thereby optimizing both bandwidth utilization and video quality.
3Stability of the object's composition
If quantization parameters are adjusted to stabilize bitrate, then bitrate stability is improved, but video quality may be compromised
Solution Approach 1:
The patent applies local quality by adjusting quantization parameters differently across video segments based on their complexity. Low-complexity segments use higher quantization parameters (coarser compression) to maintain bitrate stability without noticeably affecting quality, while high-complexity segments use lower quantization parameters (finer compression) to preserve quality even when bitrate fluctuates. This localized approach to quantization prevents uniform quality degradation.
Solution Approach 2:
The patent performs preliminary analysis of video segment complexity before encoding, allowing the bitrate stabilization algorithm to pre-determine appropriate quantization parameter ranges for upcoming segments. By anticipating complex segments in advance, the system can prepare to maintain higher bitrates or use finer quantization, preventing quality degradation before it occurs rather than reacting after the fact.
Data Source
AI summary
Techniques are generally described for bit rate stabilization. In various examples, an encoder may identify a first frame of video data to be encoded using a first quantization value. In some examples, the encoder may determine an estimated compressed frame size of the first frame encoded using the first quantization value. In various examples, the encoder may determine that the estimated compressed frame size is less than threshold size. The encoder may further determine a second quantization value that may be less than the first quantization value. The encoder may generate a compressed first frame of video data using the second quantization value and the first frame of video data. The compressed first frame may be sent to a recipient computing device.