Video Encoding Operational Points via Segment Clustering
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Solution Overview
Problem
Existing video encoding methods use static lists of encoding operational points that fail to account for the diverse characteristics of different videos and segments within a video, leading to suboptimal transcoding results, resource wastage, and irregular quality gaps.
Innovation Solution
A pre-analysis optimization process dynamically selects encoding operational points based on characteristics of video segments, using clustering and optimization techniques to generate a customized list for each portion of the video, incorporating rate distortion curves and machine learning for predicting optimal bitrates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If default or universal encoding operational points are used for all videos and segments, then the encoding process is simple and fast, but the video quality is suboptimal and resource wastage occurs due to diverse content characteristics
Solution Approach 1:
The video is divided into multiple segments, and encoding operational points are selected independently for each segment based on its specific characteristics. This allows different encoding parameters to be applied to different parts of the video, improving overall quality while maintaining efficient processing through localized optimization.
Solution Approach 2:
The encoding operational points are dynamically selected based on the characteristics of each video segment rather than using static default values. The system adapts the encoding parameters in real-time according to content complexity, motion levels, and other segment-specific features, resolving the contradiction between speed and quality.
2Productivity
If default or universal encoding operational points are used for all videos and segments, then the encoding process is simple and fast, but resource wastage occurs due to diverse content characteristics
Solution Approach 1:
Different encoding operational points are applied to different video segments based on their local characteristics. Complex segments receive more resources with higher bitrate allocations, while simple segments use lower bitrates. This localized quality adjustment eliminates resource wastage on simple content while ensuring adequate resources for complex segments.
3Device complexity
If a single set of encoding operational points is used for the entire video, then the encoding process is straightforward, but irregular quality gaps appear within the same video
Solution Approach 1:
The video is segmented and each segment receives optimized encoding parameters tailored to its characteristics. This prevents the quality gaps that occur when a single set of parameters is applied uniformly, as each segment can now be encoded at the appropriate quality level for its content requirements.
Solution Approach 2:
Encoding parameters such as bitrate, resolution, and compression settings are changed dynamically across different segments based on content analysis. This parameter adaptation ensures consistent quality throughout the video by matching encoding intensity to content complexity, rather than applying fixed parameters that cause quality variations.
Data Source
AI summary
In some embodiments, a method receives a plurality of representations of a relationship between bitrate and quality for a first portion of content. Representations in the plurality of representations are based on respective second portions of the content that is included in the first portion of the video. Clusters of the plurality of representations are generated and the clusters are analyzed to determine a first list of encoding operational points for respective clusters. The method analyzes the first list of encoding operational points for respective cluster to determine a second list of encoding operational points. The second list of encoding operational points is output for use in encoding the first portion of content.