Adjustable 2n-ary Tree Encoding for Arbitrary Resolution 3D Volume Data
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Solution Overview
Problem
Existing methods for compressing 3D volume data, such as the PointTexture format, face challenges in efficiently compressing data with arbitrary resolutions and achieving natural visualization, especially when restoring progressive bitstreams, and are limited by the need for fixed resolution compression.
Innovation Solution
The development of an adjustable quad tree, octree, and 2n-ary tree generation method allows for efficient encoding and decoding of 3D volume data with arbitrary resolutions, using modified breadth-first search and predictive coding techniques to generate bitstreams that support progressive transmission and natural visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional octree compression method is used, then compression of fixed resolution data is achieved, but data with arbitrary resolution cannot be compressed and progressive transmission is not supported
Solution Approach 1:
The patent applies dynamics by making the tree structure adjustable and adaptive rather than fixed. The adjustable octree can dynamically adjust its structure to accommodate any resolution by allowing variable numbers of children nodes (2n children instead of fixed 8), enabling the system to adapt to arbitrary resolution requirements while maintaining compression capabilities
Solution Approach 2:
The patent segments the 3D volume data into hierarchical tree structures where each node can be further divided into 2n sub-nodes. This segmentation allows the data to be processed at different resolution levels, supporting both compression of arbitrary resolution data and progressive transmission by processing nodes at different depths independently
2Manufacturing precision
If PointTexture format with high sampling density is used, then realistic visualization is achieved, but data amount becomes huge and compression becomes difficult
Solution Approach 1:
The patent segments the PointTexture data into hierarchical tree structures, allowing compression at multiple resolution levels. This segmentation enables the system to retain high visualization quality by preserving detailed information in appropriate regions while compressing redundant data, thus reducing the overall data amount without sacrificing visualization quality
Solution Approach 2:
The patent applies local quality by allowing different regions of the 3D data to be represented at different resolutions. The adjustable tree structure can allocate higher detail to regions requiring it and lower detail to regions where it is not needed, optimizing the balance between visualization quality and data quantity
3Productivity
If conventional compression method is used, then data compression is achieved, but natural visualization during progressive restoration is not enabled
Solution Approach 1:
The patent applies preliminary action by pre-establishing the hierarchical tree structure and node priorities before compression. This allows the system to prepare the data structure in advance, enabling efficient compression while also facilitating natural progressive visualization during restoration by having the structure ready for systematic processing
Solution Approach 2:
The patent implements feedback mechanisms through the hierarchical tree structure that allows progressive restoration. The system can feedback the compression and restoration process, enabling natural visualization by processing nodes in a systematic manner that maintains structural integrity and visual coherence throughout the progressive restoration
Data Source
AI summary
A method and apparatus for encoding and/or decoding depth image-based representation (DIBR) data are provided. The encoding method includes: converting 3-dimensional (3D) volume data into adjustable octree data with predetermined labels given to nodes; by referring to the labels, encoding nodes of the adjustable octree from the root node to leaf nodes by a modified breadth-first search (BFS) method allocating priorities among children nodes; and generating a bitstream with predetermined header information and encoded node data. The decoding method includes: extracting header information containing at least resolution information of an object, from a bitstream and decoding the header information; calculating the number of nodes by using the resolution information of the header, and by a modified BFS method allocating priorities among children nodes, decoding each node of a tree from the root node to leaf nodes; and restoring an adjustable tree by using decoded nodes. According to the method and apparatus, a huge amount of 3D volume data can be encoded with a high efficiency, and volume data with an arbitrary resolution as well as volume data with a fixed resolution can be encoded with a high efficiency. Accordingly, with a small amount of data, 3D volume data with a high picture quality can be restored. In addition, since a progressive bitstream is generated, 3D volume data can be shown progressively when decoding is performed.