Video-Specific Encoding Ladders Using Convex Hull and Bandwidth Data
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Solution Overview
Problem
Existing video encoding methods lack efficiency in generating encoding ladders that adapt to the specific characteristics of a video and the audience's bandwidth distribution, leading to suboptimal video quality and increased computational costs.
Innovation Solution
A method that extracts video features using a convex hull estimation model to generate a video-specific encoding ladder, selecting bitrate-resolution pairs that maximize quality and minimize costs based on audience bandwidth distributions, without requiring trial encodes of the input video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional video encoding methods are used to generate encoding ladders, then comprehensive video quality coverage is achieved, but computational costs and encoding time increase significantly
Solution Approach 1:
The patent extracts video features and generates a convex hull estimation model in advance, before actual encoding. This preliminary analysis of video characteristics (motion complexity, visual complexity, content type) allows the system to predict optimal bitrate-resolution pairs without performing exhaustive trial encodes, thereby reducing encoding time while maintaining quality coverage
Solution Approach 2:
The patent creates a simplified representation (convex hull model) that copies essential video quality characteristics without requiring full video encoding. This model serves as a surrogate that predicts quality outcomes, eliminating the need for multiple complete encoding iterations while preserving the ability to assess video quality across different bitrate-resolution combinations
2Loss of time
If video-specific encoding ladders are generated using feature extraction and convex hull estimation, then encoding time is reduced, but manufacturing precision of encoding parameter selection may be compromised
Solution Approach 1:
The patent replaces the mechanical trial-encode-evaluate process with a computational convex hull estimation model. This model uses extracted video features (motion complexity, visual complexity, content type) to mathematically determine optimal bitrate-resolution pairs, substituting physical encoding iterations with algorithmic prediction while maintaining precision
Solution Approach 2:
The patent transforms the encoding parameter selection problem into a geometric optimization problem by constructing a convex hull in the bitrate-resolution-quality space. By changing the approach from iterative encoding to geometric estimation based on video features, the system achieves accurate parameter selection without exhaustive searching
3Reliability
If audience-specific bitrate selection is implemented, then video quality for target audience is improved, but system complexity increases
Solution Approach 1:
The patent tailors the encoding ladder generation to specific audience characteristics by analyzing audience bandwidth distributions and selecting bitrate-resolution pairs optimized for that demographic. Instead of creating a universal encoding ladder, the system adapts the encoding parameters locally to match the specific needs and capabilities of the target audience, improving quality where it matters most
Solution Approach 2:
The patent performs preliminary analysis of audience characteristics and bandwidth distributions before generating the encoding ladder. This advance understanding of the target audience allows the system to pre-optimize bitrate selections, eliminating the need for complex real-time adjustments during streaming while maintaining audience-specific quality optimization
4Reliability
If comprehensive bitrate-resolution pairs are generated without audience bandwidth consideration, then video quality coverage is maximized, but loss of information about actual viewer experience occurs
Solution Approach 1:
The patent incorporates audience bandwidth distribution data as feedback into the encoding ladder generation process. By analyzing actual or predicted audience characteristics and feeding this information back into the convex hull estimation model, the system adjusts bitrate-resolution pair selections to reflect real viewer conditions, ensuring that quality coverage aligns with actual viewing capabilities rather than theoretical maximums
Data Source
AI summary
A method including: extracting a set of video features representing properties of a video segment; generating a set of bitrate-resolution pairs based on the set of video features, each bitrate-resolution pair in the set of bitrate-resolution pairs defining a bitrate and defining a resolution estimated to maximize a quality score characterizing the video segment encoded at the bitrate; accessing a distribution of audience bandwidths; selecting a top bitrate-resolution pair in the set of bitrate-resolution pairs; selecting a bottom bitrate-resolution pair in the set of bitrate-resolution pairs; selecting a subset of bitrate-resolution pairs in the set of bitrate-resolution pairs based on the distribution of audience bandwidths, the subset of bitrate-resolution pairs defining bitrates less than the top bitrate and greater than the bottom bitrate; and generating an encoding ladder for the video segment comprising the top bitrate-resolution pair, the bottom bitrate-resolution pair, and the subset of bitrate-resolution pairs.