Rate-Distortion Curve Prediction for Adaptive Video Bitrate Ladders
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Solution Overview
Problem
Existing video encoding methods using static lists of candidate average bitrates fail to optimize encoding for diverse video characteristics, leading to suboptimal transcoding results with redundant or irregular quality gaps, which can result in poor viewing experiences.
Innovation Solution
A pre-analysis optimization process dynamically selects candidate average bitrates based on the characteristics of video portions, using rate distortion curves to generate an optimized list that ensures optimal selection of transcoded segments for profiles in the profile ladder, thereby maintaining consistent quality and minimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static lists of candidate average bitrates are used for video encoding, then the encoding process is simple and fast, but the transcoding results are suboptimal with redundant or irregular quality gaps
Solution Approach 1:
The patent applies dynamics by transitioning from static bitrate lists to dynamic selection of candidate average bitrates. The system generates multiple candidate bitrate lists with different characteristics (e.g., different numbers of bitrates, different bitrate ranges) and selects the most appropriate list based on the specific video content being encoded. This allows the encoding process to adapt to varying video characteristics while maintaining efficiency.
Solution Approach 2:
The patent changes key parameters of the bitrate lists including the number of candidate bitrates, the bitrate range, and the distribution of bitrates within the list. By varying these parameters based on video characteristics (such as resolution, bitrate, and content complexity), the system optimizes transcoding quality without sacrificing encoding speed.
2Manufacturing precision
If dynamic selection of candidate average bitrates is implemented, then transcoding quality is optimized, but the complexity of the encoding process increases
Solution Approach 1:
The patent applies preliminary action by pre-generating multiple candidate bitrate lists with different characteristics before the actual encoding process. These candidate lists are prepared in advance based on expected video characteristics, so that during encoding, the system only needs to select from the pre-prepared options rather than generating optimized lists in real-time. This reduces the computational complexity during the actual encoding phase.
Solution Approach 2:
The patent introduces an intermediary component that acts as a bridge between the video input and the encoding process. This intermediary analyzes video characteristics and selects the most appropriate candidate bitrate list from the pre-generated options. By separating the complex optimization logic into this intermediary selection layer, the actual encoding process remains relatively simple while still benefiting from optimized bitrate selection.
3Manufacturing precision
If multiple candidate bitrate lists are generated and selected dynamically, then quality gaps between profiles are minimized, but the computational resources required increase
Solution Approach 1:
The patent applies segmentation by dividing the candidate bitrate selection into multiple discrete lists, each optimized for specific video characteristics (e.g., different resolutions, different content types). Instead of generating one comprehensive optimized list, the system segments the bitrate options into manageable categories and selects from the appropriate segment based on the input video characteristics. This reduces the computational burden of generating and managing a single large optimized list.
Solution Approach 2:
The patent applies local quality by creating candidate bitrate lists with different characteristics suited to different video content types. Rather than using a uniform approach for all videos, the system tailors the candidate list characteristics (number of bitrates, bitrate range, distribution) to match the specific local requirements of each video segment or type, optimizing quality while managing computational resources efficiently.
Data Source
AI summary
In some embodiments, a method determines feature values for a portion of a video and a target configuration. The target configuration is associated with a parameter of an encoder and includes a set of bitrates and a resolution. A plurality of quality values is generated for the set of bitrates and the resolution based on the feature values. The method generates a representation of a relationship between bitrate and the plurality of quality values for the resolution. The representation is analyzed to determine a list of bitrates for the portion of the video. The method outputs he list of bitrates for use encoding the portion of the video using the resolution.