Point Cloud Encoding with Scaling and Inter-Prediction
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Solution Overview
Problem
Existing methods for processing point cloud data face challenges in efficiently transmitting and receiving large amounts of data, leading to issues with latency, encoding/decoding complexity, and compression performance.
Innovation Solution
A method and apparatus for efficiently transmitting and receiving point cloud data by encoding geometry information, selectively applying a scaling factor to attribute information, and performing inter-prediction based on reference and current frames, along with signaling information that includes scaling-related data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional point cloud data transmission methods are used, then data can be transmitted, but compression performance is poor and latency is high
Solution Approach 1:
The patent applies preliminary action by performing inter-frame prediction using reference frames before actual transmission. The encoder predicts attribute information from reference frames and transmits only the residual data, significantly reducing the amount of data to transmit and improving compression performance while reducing latency.
Solution Approach 2:
The patent implements feedback mechanisms where the receiver uses received geometry information and scaling factors to reconstruct attribute information through inter-prediction. The feedback loop allows continuous optimization of compression parameters based on actual transmission conditions, improving both compression efficiency and reducing latency.
2Productivity
If attribute information is encoded without scaling, then encoding is simple, but compression efficiency is low
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting scaling factors based on the intensity and distribution of attribute information. The encoder calculates scaling factors that optimize compression efficiency for different types of attribute data (e.g., color, reflectance), achieving better compression without excessive complexity through adaptive parameter modification.
Solution Approach 2:
The patent implements local quality by applying different scaling factors to different attribute information types and spatial regions. The scaling is adapted to local characteristics of the point cloud data, such as varying intensity distributions in different areas, which improves compression efficiency while maintaining manageable encoding complexity through localized optimization.
3Productivity
If no inter-prediction is used, then encoding is simpler, but compression performance deteriorates
Solution Approach 1:
The patent applies preliminary action by performing inter-frame prediction using reference frames before actual transmission. The encoder predicts attribute information from reference frames and transmits only the residual data, significantly reducing the amount of data to transmit and improving compression performance while reducing latency.
Solution Approach 2:
The patent implements feedback mechanisms where the receiver uses received geometry information and scaling factors to reconstruct attribute information through inter-prediction. The feedback loop allows continuous optimization of compression parameters based on actual transmission conditions, improving both compression efficiency and reducing latency.
Data Source
AI summary
A point cloud data transmission method according to embodiments comprises the steps of: encoding geometry information of point cloud data; encoding attribute information of the point cloud data on the basis of the geometry information; and transmitting the encoded geometry information, the encoded attribute information, and signaling information, wherein the step of encoding the attribute information may comprise the steps of: selectively applying a scaling factor to the attribute information; and compressing the attribute information by performing inter prediction on the basis of a reference frame and the current frame including attribute information to which the scaling factor is or is not applied.