Hierarchical Point Cloud Attribute Encoding for Scalable Decoding
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Solution Overview
Problem
Existing methods for encoding 3D point cloud data face inefficiencies in encoding efficiency, particularly when trying to balance scalable decoding with the need to reduce data size, as methods like lifting may not cope well with scalable decoding while others, like those using octrees, may reduce encoding efficiency, especially in sparse point conditions.
Innovation Solution
An information processing device and method that hierarchize attribute information of point clouds by recursively classifying points as predictive or reference points, using multiple hierarchization methods at different levels to derive predictive values and difference values, allowing for flexible switching between methods that do or do not support scalable decoding, thereby optimizing encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a method employing the technique described in NPL 3 (scalable decoding method) is used, then scalable decoding capability is improved, but encoding efficiency deteriorates
Solution Approach 1:
The patent applies dynamics by making the hierarchization method selectable and switchable based on requirements. The encoding device can dynamically choose between first hierarchization method (for scalable decoding) and second hierarchization method (for encoding efficiency) depending on the specific application scenario, resolution requirements, and point cloud density, thus resolving the contradiction between scalable decoding capability and encoding efficiency.
Solution Approach 2:
The patent changes the parameter of hierarchization method selection based on point cloud characteristics. By analyzing point density, resolution requirements, and scalability needs, the system adjusts which hierarchization method to apply, transforming a static method selection into a dynamic parameter adjustment process that optimizes both scalable decoding capability and encoding efficiency under different conditions.
2Productivity
If the second hierarchization method is used for all levels, then encoding efficiency is improved, but scalable decoding capability deteriorates
Solution Approach 1:
The patent applies local quality by assigning different hierarchization methods to different levels of the hierarchization structure. Specifically, the first hierarchization method is applied to hierarchization levels that require scalable decoding capability, while the second hierarchization method is applied to levels where encoding efficiency is the priority, thus creating local optimization rather than uniform application across all levels.
3Device complexity
If a single hierarchization method is used for all levels, then device complexity is reduced, but encoding efficiency and scalable decoding capability deteriorate
Solution Approach 1:
The patent applies segmentation by dividing the hierarchization process into multiple independent levels, each capable of using different hierarchization methods. This segmentation allows the system to manage complexity through modular design while achieving superior overall performance, as each level can be optimized independently based on its specific requirements rather than forcing a single method across all levels.
Data Source
AI summary
The present disclosure relates to an information processing device and method capable of curbing reduction in encoding efficiency. In an information processing device for processing attribute information of each point of a point cloud representing an object in a three-dimensional shape as a set of points, a first level is hierarchized using a first hierarchization method and a second level different from the first level is hierarchized using a second hierarchization method different from the first hierarchization method at the time of performing hierarchization of attribute information by recursively repeating processing of, among points classified as a predictive point or a reference point, deriving a difference value between a predictive value of attribute information of the predictive point derived using attribute information of the reference point and the attribute information of the predictive point for the reference point to perform hierarchization of the attribute information. The present disclosure is applicable, for example, to an information processing device, an image processing device, an encoding device, a decoding device, an electronic apparatus, an information processing method, a program, or the like.