In-Loop Filter Parameter Training for Videoconference Video Coding
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Solution Overview
Problem
Existing video encoding techniques for videoconferencing systems face challenges in achieving high coding efficiency while maintaining video quality, particularly due to the use of generic in-loop filter parameters that do not account for the specific characteristics of videoconferencing content.
Innovation Solution
Deriving in-loop filter parameters through training using video sequences similar to those encountered in videoconferencing, such as varying backgrounds, lighting conditions, and foreground subjects, to optimize the filtering process for improved visual quality and reduced bit rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If generic in-loop filter parameters are used for video encoding, then device complexity is reduced and ease of operation is improved, but video quality deteriorates and coding efficiency is limited
Solution Approach 1:
The patent applies preliminary action by pre-training the in-loop filter parameters using representative videoconferencing content before actual encoding. The training phase establishes optimized filter parameters that are then reused during video encoding, eliminating the need for complex real-time parameter adjustment while maintaining high video quality. This advance preparation resolves the contradiction by achieving precision through pre-computed parameters without increasing operational complexity.
Solution Approach 2:
The patent utilizes parameter changes by adapting filter parameters based on specific videoconferencing characteristics such as frame size, frame rate, and content type. The training process adjusts parameters like filter strength, kernel size, and application thresholds to match videoconferencing requirements. This targeted parameter optimization improves video quality specifically for videoconferencing applications without requiring complex adaptive mechanisms during encoding.
2Productivity
If training is performed to derive optimized in-loop filter parameters, then coding efficiency is improved and video quality is enhanced, but loss of time increases due to the training process
Solution Approach 1:
The training process is performed as a preliminary one-time operation before deployment. Once the optimized parameters are derived from training on representative videoconferencing content, they are stored and reused for all subsequent encoding operations. This eliminates repeated training time while maintaining high coding efficiency during actual video encoding, as the parameters are already optimized for the specific application.
Solution Approach 2:
The patent creates a copy of optimized parameters derived from training on representative videoconferencing content. These parameter sets are then applied to multiple video encoding operations without requiring re-training. The copied parameters maintain their optimization benefits across different videoconferencing sessions, reducing the time loss to a single initial training phase while preserving coding efficiency gains.
3Adaptability or versatility
If in-loop filter parameters are tailored to videoconferencing characteristics, then adaptability to the specific application is improved, but device complexity increases
Solution Approach 1:
The patent performs the complex parameter adaptation work in advance through training on representative videoconferencing content. The training phase analyzes various videoconferencing characteristics (frame sizes, resolutions, content types) and derives optimized parameters for each scenario. During actual encoding, the system simply selects from pre-determined parameter sets based on matching video characteristics, achieving high adaptability without complex real-time processing.
Solution Approach 2:
The patent implements parameter changes by creating multiple predefined parameter sets tailored to different videoconferencing scenarios (e.g., different frame sizes, resolutions, content types). The system selects the appropriate parameter set based on the current video characteristics rather than dynamically adjusting parameters in real-time. This approach achieves application-specific optimization while keeping the encoding process simple and efficient.
Data Source
AI summary
Deriving in-loop filter parameters via training for video encoding is provided. A video encoder performs inter prediction for a frame in a set of frames of the video to generate prediction residuals for the frame. The inter prediction for the frame is performed based on a reconstructed frame in the set of frames filtered using an in-loop filter. The value of a parameter of the in-loop filter is determined by determining, for each candidate in-loop filter parameter value, a visual quality metric for a set of training frames in training video sequences filtered by the in-loop filter. The candidate in-loop filter parameter value that corresponds the highest visual quality metric can be selected as the value of the parameter of the in-loop filter. The video encoder further encodes the prediction residues of the frame and the parameter of the in-loop filter into a bitstream representing the video.