Adaptive Depth Prediction for Point Cloud Coding Discontinuities
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Solution Overview
Problem
Existing methods for coding and decoding point cloud depth information fail to consider scenario discontinuity, leading to low prediction accuracy, numerous outliers, and reduced coding efficiency due to reliance on neighboring point predictions.
Innovation Solution
Create an adaptive prediction list comprising multiple candidate lists (first, second, third, and fourth candidate lists) to store depth information values from same/different lasers, historically coded points, and priori values, selecting optimal values to form an adaptive prediction list for efficient coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If depth information of neighboring points is used for prediction, then prediction process is simple, but prediction accuracy is low and outliers increase due to scenario discontinuity
Solution Approach 1:
The prediction list is dynamically updated based on coded depth values from multiple lasers and historical points. The list adapts to scenario discontinuities by selectively incorporating relevant depth information, allowing the prediction mechanism to respond to changing spatial contexts while maintaining computational efficiency.
Solution Approach 2:
The prediction list combines depth information from multiple sources (same laser, different lasers, historical points) into a composite structure. This composite approach integrates diverse spatial references to improve prediction accuracy while handling scenario discontinuities that single-source prediction cannot address.
2Measurement precision
If adaptive prediction list with multiple candidate lists is created, then prediction accuracy improves, but coding complexity increases
Solution Approach 1:
The prediction process is segmented into multiple candidate lists (first candidate list from same laser, second from different lasers, third from historical points). Each segment handles a specific spatial reference type, allowing systematic organization of prediction sources while improving overall accuracy through structured selection.
Solution Approach 2:
The system changes the parameter of prediction sources by incorporating depth values from multiple lasers and historical points rather than relying on a single neighboring point. This parameter expansion increases prediction accuracy while the structured candidate list approach manages the associated complexity.
3Reliability
If multiple candidate lists are used for prediction, then outliers and hop values decrease, but data processing time increases
Solution Approach 1:
Candidate lists are pre-organized during the coding process, with depth values from multiple lasers and historical points prepared in advance. This preliminary organization reduces the time required for prediction by having structured data ready for selection, rather than searching for relevant points during prediction.
Solution Approach 2:
The adaptive prediction list automatically selects and updates itself based on coded depth values, reducing the need for external intervention or complex search processes. The system serves itself by maintaining an up-to-date prediction structure that adapts to the coding progress, improving reliability without proportionally increasing processing time.
Data Source
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AI summary
The present invention discloses a method and an apparatus for predictively coding and decoding depth information of a point cloud. A method for coding includes: obtaining original point cloud data; creating an adaptive prediction list of the depth information of the point cloud; and predictively coding the depth information of the point cloud based on the adaptive prediction list, to obtain codestream information. According to the present invention, an adaptively updated prediction list of the depth information is created, and an optimal prediction mode is selected from the list to predict the depth information of the point cloud. Therefore, discontinuity of the depth information of the point cloud caused by discontinuity of an actual scenario is resolved. Accordingly, a predicted residual and a frequency of occurrence of an outlier and a hop value, of the depth information, are significantly reduced, and prediction accuracy and coding efficiency are improved.