Custom Bitrate Ladder Generation via Probe Encoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video encoding approaches face inefficiencies in determining optimal bitrate ladders for diverse content types, leading to suboptimal encoding tradeoffs, especially for publishers with large and varied content libraries, as conventional methods rely on empirical or genre-based bitrate ladders that fail to provide tailored encoding for each title.
Innovation Solution
A computer-implemented method generates a custom bitrate ladder by probe encoding the input video to create a bitrate table mapping resolutions to mean bitrates, determining optimal resolutions based on distance scores, and selecting resolutions with the smallest distance to achieve constant quality, allowing for a tailored encoding approach for each video title.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional empirical or genre-based bitrate ladders are used for encoding, then encoding process is simple and fast, but encoding quality and efficiency are suboptimal for diverse content types
Solution Approach 1:
The system performs preliminary probe encoding to analyze video characteristics before determining the optimal bitrate ladder. This preliminary analysis includes encoding test segments and measuring quality metrics to establish content-specific encoding parameters, thereby improving final encoding quality without requiring complex manual configuration
Solution Approach 2:
The system dynamically adjusts bitrate ladder parameters based on measured video characteristics such as complexity, motion content, and scene changes. By changing encoding parameters according to actual content properties rather than using fixed genre-based ladders, the system achieves optimal quality-efficiency tradeoffs for diverse content types
2Manufacturing precision
If multiple encodings are performed to determine optimal bitrate ladder, then encoding quality improves, but encoding time and computational cost increase significantly
Solution Approach 1:
The system performs probe encoding on selected key frames or representative segments rather than encoding entire videos multiple times. By analyzing a partial set of frames that capture essential video characteristics, the system determines optimal bitrate ladders with minimal additional encoding time while maintaining optimization accuracy
Solution Approach 2:
The system implements iterative feedback loops where probe encoding results are measured, analyzed, and used to adjust bitrate ladder parameters. Quality metrics from probe encoding feed back into the optimization process, allowing the system to converge on optimal parameters with minimal encoding iterations rather than requiring multiple full encodings
3Productivity
If generic bitrate ladders are used for all video types, then encoding process is efficient, but quality tradeoffs are suboptimal for videos significantly different from average content
Solution Approach 1:
The system determines separate bitrate ladder parameters for different segments or scenes within a video based on their local characteristics. By identifying regions with different complexity levels, motion patterns, or visual importance, the system applies optimized encoding parameters locally rather than using a single generic ladder for the entire video, thereby improving quality tradeoffs while maintaining efficiency
Data Source
AI summary
An original input content is subjected to multiple constant quality probe encodes for a defined set of resolutions. In one embodiment, probe encodes encode a few parts of the original source video, for example, 30 seconds from 5 different positions. Each probe encode delivers an average bitrate that is required to achieve the configured constant quality. The mean value of the average bitrate is taken per resolution. This results in a list of bitrates that map to a resolution that would achieve the best quality, a custom bitrate table. Based on the custom bitrate table, an optimized bitrate ladder is computed. The process starts with a configurable minimum bitrate and steps up by a bitrate step size that is between a configurable min and max bitrate step size until the bitrate of the highest resolution is reached.