Point Cloud Residual Coding for Faster Low-Complexity Compression
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Solution Overview
Problem
The existing encoding methods for point clouds face challenges in improving encoding speed and reducing encoding complexity, particularly in handling large volumes of geometric and attribute information.
Innovation Solution
The method determines residual values based on the original and reconstructed geometric position information of candidate points, determines the codeword length, and encodes the residual values, utilizing a processor and computer-readable storage medium to execute the encoding process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional entropy encoding algorithms are used for point cloud compression, then compression capability is achieved, but encoding speed is slow and encoding complexity is high
Solution Approach 1:
The patent segments the point cloud data processing into distinct stages: octree partitioning for geometric information, followed by separate entropy encoding processes. By dividing the encoding task into manageable segments with different encoding strategies, the system achieves faster processing while maintaining compression effectiveness.
Solution Approach 2:
The patent changes the encoding parameters by using different entropy encoding algorithms for different types of data. Specifically, it uses one entropy encoding algorithm for geometric information and another for attribute information, optimizing the encoding process for each data type to improve overall encoding speed and reduce complexity.
2Loss of information
If detailed geometric and attribute information is compressed, then compression effectiveness is achieved, but the processing complexity increases
Solution Approach 1:
The patent separates the compression process into two independent streams: one for geometric information (using octree partitioning) and another for attribute information (using prediction and residual encoding). This segmentation allows each stream to be optimized independently, reducing overall processing complexity while maintaining comprehensive compression effectiveness.
Solution Approach 2:
The patent introduces intermediate processing steps such as prediction models and residual calculation mechanisms that act as mediators between the original data and the final compressed representation. These intermediaries simplify the encoding process by transforming complex data relationships into more manageable forms before entropy encoding.
Data Source
AI summary
Embodiments of the present application provide a coding method, a coder, an electronic device, and a storage medium. The coding method comprises: determining at least one residual value on the basis of an original value of geometric position information of a current point and a reconstruction value of geometric position information of at least one candidate point, the at least one residual value comprising a first residual value; before coding the first residual value, determining the codeword length of the at least one residual value; on the basis of the codeword length of the at least one residual value, determining that a predicted residual value of the geometric position information of the current point comprises the first residual value; and coding the first residual value.