Cloud VR Video Encoding Using IMU Sensor Data
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Solution Overview
Problem
Cloud VR systems face high computational burdens and latency due to the time-consuming process of video encoding, especially in high-resolution virtual reality applications, where existing hardware-accelerated encoders struggle to meet the minimum requirements for user experience.
Innovation Solution
The proposed solution utilizes IMU data from Head Mounted Displays to enhance video encoding by selecting optimal reference frames and prediction modes, reducing the computational overhead in motion estimation and improving compression rates, thereby accelerating the encoding process and enhancing video quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If video compression is increased to reduce bandwidth usage, then transmission efficiency is improved, but processing time increases
Solution Approach 1:
The system performs preliminary action by predicting the current video frame using IMU sensor data and motion estimation before actual encoding. By pre-calculating motion vectors and reference frame selections based on sensor information, the system reduces the computational burden during the actual compression process, thereby decreasing processing time while maintaining high compression ratios
Solution Approach 2:
The patent substitutes traditional mechanical video analysis methods with sensor-based prediction. Instead of analyzing actual pixel differences between frames, the system uses IMU sensor data (accelerometers, gyroscopes, magnetometers) to predict frame changes, replacing computationally intensive mechanical image processing with more efficient sensor-driven calculations
2Manufacturing precision
If high resolution video encoding is performed to improve video quality, then video quality is improved, but computational burden and processing time increase
Solution Approach 1:
The system performs preliminary frame prediction using IMU data before high-resolution encoding. By pre-determining motion vectors, reference frames, and prediction modes based on sensor information, the system reduces the computational complexity of high-resolution encoding, enabling faster processing without sacrificing video quality
Solution Approach 2:
The patent changes encoding parameters dynamically based on IMU sensor data. By adjusting motion estimation search ranges, reference frame selections, and prediction mode choices according to actual sensor readings, the system optimizes encoding efficiency for high-resolution video, reducing processing time while maintaining superior video quality
3Loss of energy
If motion estimation is performed with high accuracy to improve compression ratio, then compression efficiency is improved, but processing time increases
Solution Approach 1:
The system replaces traditional mechanical block-matching motion estimation with sensor-based prediction. By using IMU data to directly predict motion vectors and frame changes, the system achieves high compression ratios without the computationally intensive process of analyzing pixel differences across the entire frame
Solution Approach 2:
The patent optimizes motion estimation parameters by leveraging IMU sensor data. By adjusting search ranges, reference frame selections, and prediction modes based on actual sensor readings, the system achieves accurate motion estimation faster, improving compression efficiency while reducing processing time
Data Source
AI summary
An encoding acceleration method of cloud VR (virtual reality) video, the method comprising: constructing a reference frame candidate for encoding of a current frame; selecting a specific reference frame among the reference frame candidate based on a sensor data of IMU (Inertial Measurement Unit); selecting a prediction mode for encoding the current frame based on information included in the specific reference frame; and encoding the current frame based on the selected prediction mode.