3D Point Cloud Encoding via Overlap Frame Inter Prediction
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Solution Overview
Problem
The large data size of 3D point cloud data poses challenges for efficient encoding and compression, particularly in applications like real-time 3D immersive telepresence and navigation services, where existing methods fail to effectively reduce the data size while maintaining quality.
Innovation Solution
An encoding device performs inter prediction operations using an overlap frame generated by a union operation of reference frames, which includes motion compensation detection points from different time periods, to create a bitstream that efficiently encodes 3D point cloud data collected by LiDAR devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If inter prediction operation is performed using a single reference frame, then the encoding process is simple, but the compression efficiency is insufficient due to limited detection points
Solution Approach 1:
The patent merges multiple reference frames (first reference frame and second reference frame) into a single overlap frame by combining detection points from both frames. This allows the inter prediction operation to utilize a larger set of detection points, improving compression efficiency while maintaining manageable encoding complexity through systematic integration of reference data.
2Productivity
If more detection points are considered in inter prediction, then compression efficiency improves, but the data processing complexity increases
Solution Approach 1:
The patent performs preliminary generation of the overlap frame by pre-integrating detection points from multiple reference frames before the inter prediction operation. This preliminary action organizes the additional detection points into a structured format, enabling improved compression efficiency while controlling processing complexity through advance preparation of the combined reference data.
3Measurement precision
If motion compensation is applied from multiple reference frames, then the accuracy of prediction improves, but the computational load increases
Solution Approach 1:
The patent combines motion compensation results from multiple reference frames by merging detection points with motion vectors into a unified overlap frame. This approach improves prediction accuracy by incorporating diverse motion information while managing computational energy through efficient integration rather than separate processing of each reference frame.
Data Source
AI summary
Disclosed is an encoder which receives first to third input frames included first intra period and outputs a bitstream corresponding to the third input frame. The encoder includes a motion compensation unit that generates a first reference frame corresponding to the first input frame and a second reference frame corresponding to the second input frame, a union operation unit that generates an overlap frame by performing a union operation based on the first reference frame and the second reference frame, and an inter prediction unit that generates an occupancy code by performing an inter prediction operation on the overlap frame and the third input frame. In this case, the bitstream includes the occupancy code.