Variable Framerate Encoding Using Content-Aware Prediction
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Solution Overview
Problem
High framerate video streaming for UHDTV increases encoding and decoding complexities, leading to high bit-rate requirements and temporal artifacts when conventional techniques drop frames or use limited variable framerate coding schemes.
Innovation Solution
A method for variable framerate encoding using content-aware framerate prediction, which extracts spatial and temporal energy features to predict a frame dropping factor and optimized framerate for each shot, allowing for efficient encoding and decoding while maintaining video quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high framerate encoding is used for UHDTV, then video quality and immersive experience are improved, but encoding complexity and bit-rate requirements increase significantly
Solution Approach 1:
The video stream is segmented into multiple shots, and each shot is further divided into segments for independent framerate optimization. This allows different portions of the video to use different framerates based on their content characteristics, reducing overall encoding complexity while maintaining quality where needed.
Solution Approach 2:
The encoding system dynamically adjusts the framerate for each shot based on content-aware features such as motion magnitude and scene complexity. Rather than using a fixed high framerate throughout, the system adapts the framerate in real-time to match the actual content requirements, reducing encoding complexity while preserving quality.
2Productivity
If frames are dropped to reduce bit-rate, then encoding efficiency is improved, but temporal artifacts are introduced when video is upscaled
Solution Approach 1:
Different quality levels are applied to different portions of the video based on content characteristics. Shots with low motion and simple scenes use lower framerates (higher quality preservation), while complex high-motion shots use higher framerates (more frames dropped). This local adaptation reduces overall bit-rate while minimizing temporal artifacts in critical areas.
Solution Approach 2:
The system changes the framerate parameter dynamically based on content-aware analysis of each shot. By adjusting the framerate parameter according to motion magnitude, scene complexity, and other features, the system optimizes the balance between encoding efficiency and video quality, reducing temporal artifacts through intelligent parameter selection.
3Manufacturing precision
If conventional variable framerate coding schemes are used, then some distortion is reduced, but the system remains limited to three framerates and uses complex two random forest classifiers without considering bitrate
Solution Approach 1:
The system uses a dynamic framerate selection mechanism that can choose from multiple framerates (not limited to three) based on real-time content analysis. The framerate for each shot is determined by evaluating content features and predicting the optimal framerate, allowing continuous adaptation rather than discrete classification into fixed categories.
Solution Approach 2:
The encoding system incorporates feedback loops where content-aware features are extracted from each shot, used to predict the optimal framerate, and then the encoding parameters are adjusted accordingly. This feedback mechanism allows the system to consider bitrate constraints and continuously optimize the balance between quality and compression efficiency.
Data Source
AI summary
The technology described herein relates to variable framerate encoding. A method for variable framerate encoding includes receiving shots, as segmented from a video input, extracting features for each of the shots, the features including at least a spatial energy feature and an average temporal energy, predicting a frame dropping factor for each of the shots based on the spatial energy feature and the average temporal energy, predicting an optimized framerate for each of the shots based on the frame dropping factor, downscaling and encoding each of the shots using the optimized framerate. The encoded shots may then be decoded and upscaled back to their original framerates.