Content-Adaptive Video Resolution for Single-Pass Encoding
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Solution Overview
Problem
Existing video compression methods, particularly in adaptive bitrate streaming, face challenges in achieving optimal quality due to high computational complexity and inefficiencies in determining the ideal encoding resolution for different content types, leading to suboptimal quality in high or low complexity videos.
Innovation Solution
A single-pass encoding solution that uses a content adaptive resolution decision approach, leveraging machine learning to estimate distortions from quantization and downscaling, allowing for near-optimal resolution determination for a target bitrate, reducing computational complexity and enabling efficient encoding for online/live content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional multi-pass encoding methods are used to determine optimal resolution, then encoding quality can be improved, but computational complexity increases significantly
Solution Approach 1:
The patent extracts video content features (such as spatial frequency, motion complexity, and texture characteristics) before the encoding process to predict the optimal encoding resolution. This preliminary analysis allows the system to determine the best resolution without performing multiple encoding passes, thereby reducing computational complexity while maintaining encoding quality.
Solution Approach 2:
The patent replaces the traditional mechanical multi-pass encoding approach with a machine learning-based prediction system. Instead of actually encoding video at multiple resolutions and comparing results (mechanical process), the system uses trained models to predict the optimal resolution based on content features, significantly reducing computational requirements while achieving similar or better quality results.
2Productivity
If content adaptive resolution decision is implemented, then near-optimal encoding efficiency is achieved, but additional processing steps are required
Solution Approach 1:
The patent merges the content analysis and resolution decision-making processes into a single integrated workflow. The feature extraction and resolution prediction are combined into one processing stage that occurs before encoding, eliminating the need for separate analysis and decision steps. This integration maintains near-optimal encoding efficiency while reducing the number of distinct processing steps compared to traditional multi-pass methods.
3Measurement precision
If machine learning based distortion estimation is used, then resolution determination accuracy is improved, but computational overhead increases
Solution Approach 1:
The patent applies different levels of analysis to different regions of the video content based on their complexity characteristics. For simple regions with low spatial frequency and minimal motion, the system uses lighter-weight prediction models. For complex regions with high spatial frequency and significant motion, more sophisticated analysis is applied. This local quality approach improves overall resolution determination accuracy while reducing total computational overhead by avoiding uniform high-cost analysis across all content.
Data Source
AI summary
A single-pass encoding solution can be implemented to determine a suitable resolution for a target bitrate based on the characteristics of the content. One insight for determining the resolution for a target bitrate is to balance quantization-caused distortion and downscaling-caused distortion for a given video. If quantization-caused distortion is expected to be higher than the downscaling-caused distortion for a given video (e.g., such as a high complexity video), a lower resolution may be selected to encode the video for a target bitrate. If downscaling-caused distortion is expected to be higher than the quantization-caused distortion for a given video (e.g., such as a low complexity video), a higher resolution may be selected to encode the video for a target bitrate.