Video Encoding Segmentation and Bit Allocation
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Solution Overview
Problem
Current distributed video encoding systems face challenges in accurately placing I-frames and allocating bits per frame, leading to suboptimal video quality and encoding efficiency, especially when dealing with variable complexity segments and camera movements.
Innovation Solution
The system employs a complexity analyzer to allocate bits per frame based on segment complexity, splits videos into overlapping segments for precise I-frame placement, and uses multiple encoders to encode segments with different parameters, while also performing image stabilization to improve encoding quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If distributed video encoding systems use traditional encoding methods, then encoding speed is maintained, but video quality and encoding efficiency deteriorate due to inaccurate I-frame placement and suboptimal bit allocation
Solution Approach 1:
The video is divided into multiple segments that are processed independently by different encoders. Each segment is analyzed for complexity and allocated bits accordingly, enabling parallel processing while maintaining accurate I-frame placement through coordinated segment boundaries
Solution Approach 2:
The system performs preliminary complexity analysis on video segments before encoding to determine optimal bit allocation and I-frame placement strategies. This advance analysis allows encoders to be configured with precise parameters before processing, improving both accuracy and efficiency
2Manufacturing precision
If bits are allocated uniformly across all video frames, then encoding simplicity is maintained, but video quality deteriorates due to inability to handle variable complexity segments
Solution Approach 1:
The system applies different bit allocation strategies to different video segments based on their local complexity characteristics. High-complexity segments receive more bits while low-complexity segments receive fewer bits, optimizing overall video quality through localized adaptation rather than uniform treatment
Solution Approach 2:
The complexity analyzer dynamically adjusts encoding parameters including bit allocation and I-frame frequency based on measured video complexity. This adaptive parameter changing allows the system to optimize quality for each segment's specific characteristics without manual intervention
3Productivity
If multiple encoders process video segments independently, then encoding speed is improved, but quality uniformity deteriorates due to lack of coordination between segments
Solution Approach 1:
The master encoder receives feedback from complexity analysis results and coordinates I-frame placement across multiple encoder segments. This feedback mechanism ensures that I-frames are positioned at optimal locations that maintain quality uniformity while allowing parallel processing to proceed
Solution Approach 2:
Multiple encoder outputs are merged by the master encoder with coordinated I-frame placement. The system combines segments from multiple encoders while ensuring quality uniformity through centralized control of I-frame positioning and bit allocation across the merged output
4Manufacturing precision
If I-frames are placed frequently to improve quality, then video quality is improved, but bandwidth consumption increases due to higher bitrate requirements
Solution Approach 1:
The system dynamically adjusts I-frame placement frequency based on video complexity and segment characteristics. I-frames are placed more frequently in high-complexity segments where they provide greater quality benefit, while low-complexity segments use fewer I-frames, optimizing the quality-bandwidth tradeoff adaptively
Data Source
AI summary
Various of the disclosed embodiments relate to multiple video encoders that are used to simultaneously encode a video using encoders configured using different encoding parameters. A segment selector selects an encoded version of the encoded video segment using operational criteria such as video quality and bandwidth. A configuration determination module may analyze the video segment to make a decision about which encoding parameter configurations may be suitable for encoding the video segment. The configuration determination module may be trainable, based on past encoding results.