Layered 3D Attribute Quantization to Reduce Rounding Errors
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Solution Overview
Problem
Existing methods for encoding three-dimensional data, such as point cloud data, are inefficient and lack the ability to efficiently transmit and receive required information for applications, particularly in systems involving multiple codecs and formats.
Innovation Solution
A three-dimensional data encoding method that involves generating a layered structure for attribute information, quantizing coefficient values using shifted parameters, and inverse-quantizing to reduce rounding errors and calculation complexity, enabling efficient encoding and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional quantization methods are used for encoding three-dimensional data, then encoding can be performed, but rounding errors accumulate and calculation complexity increases
Solution Approach 1:
The patent changes the quantization parameter by introducing a shift amount that varies according to the node level in the layered structure. Specifically, the quantization step size is adjusted based on the depth of the node in the hierarchy, allowing coarser quantization at lower levels and finer quantization at higher levels, which optimizes the balance between precision and calculation complexity
Solution Approach 2:
The patent segments the three-dimensional space into a layered hierarchical structure (such as an octree), where different quantization strategies can be applied to different levels. This segmentation allows the system to handle complex spatial data by breaking it down into manageable layers, each with appropriate quantization parameters
2Measurement precision
If detailed attribute information is encoded for all nodes, then data accuracy is maintained, but data transmission bandwidth increases
Solution Approach 1:
The patent applies local quality by using different quantization precision levels for different regions of the three-dimensional space. Nodes at higher levels (coarser granularity) use coarser quantization, while nodes at lower levels (finer granularity) use finer quantization. This allows the system to maintain high accuracy where needed while reducing data volume in less critical regions
Solution Approach 2:
The patent implements partial action by selectively applying detailed quantization only where necessary based on the hierarchical level and importance of the node. Not all nodes receive the same level of processing detail, which optimizes the trade-off between data accuracy and transmission bandwidth by providing just enough precision for each region
Data Source
AI summary
A three-dimensional data encoding method includes: generating quantized values by quantizing, using one or more first parameters for one or more groups to which one or more attribute information belong, a coefficient value calculated for each node included in a layered structure of three-dimensional points. At least one attribute information belongs to any of the one or more groups. The calculating includes calculating, using two first coefficient values of two first nodes of a first layer, a second coefficient value of a second node of a second layer which is one layer higher. The quantizing includes: shifting-up the one or more first parameters by N bits (N is a natural number); shifting-down, by one bit, a sum of two second parameters of the two first nodes; shifting-down the result by N bits; and quantizing the second coefficient value using the obtained parameter.