Point Cloud Octree Decoding With Selectable Processing Order
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Solution Overview
Problem
Decoding encoded octree data in a breadth-first order can lead to increased memory requirements and suboptimal processing, depending on hardware performance, limiting flexibility and efficiency.
Innovation Solution
Encode the octree corresponding to point cloud data after initializing the context for each layer, allowing for decoding in either a breadth-first or depth-first order, independent of the previous node's processing results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If decoding is performed in a breadth-first order, then the encoded data can be decoded following the encoding order, but the memory capacity required for storing data becomes larger
Solution Approach 1:
The patent makes the decoding order dynamic and adaptable by allowing selection between breadth-first order and depth-first order based on hardware capabilities and performance requirements. This dynamic approach resolves the contradiction by enabling the system to switch decoding strategies rather than being fixed to a single order that inherently requires more memory.
Solution Approach 2:
The patent changes the parameter of decoding order from a fixed breadth-first approach to a selectable parameter that can be set to either breadth-first or depth-first order. This parameter change allows optimization of memory usage by selecting depth-first order when appropriate, while maintaining decoding accuracy through context initialization for each layer.
2Reliability
If decoding is performed in a breadth-first order, then the processing follows the encoding sequence, but the processing order may not be optimal depending on hardware performance
Solution Approach 1:
The patent introduces dynamic adaptability in decoding order selection, allowing the system to adjust between breadth-first and depth-first processing based on hardware performance characteristics. This resolves the contradiction by enabling optimization of processing efficiency without sacrificing decoding correctness, as both orders are supported with appropriate context management.
Solution Approach 2:
The decoding order is transformed from a fixed parameter to a configurable parameter that can be optimized based on hardware capabilities. By allowing selection of depth-first order when suitable for the hardware, the system can achieve better processing efficiency while maintaining correctness through proper context initialization for each layer.
3Device complexity
If context is not initialized for each layer, then processing is simpler, but decoding cannot be performed in various processing orders
Solution Approach 1:
The patent segments the context management by initializing context separately for each layer of the octree. This segmentation allows different processing orders (breadth-first and depth-first) to be supported independently, as each layer's context is self-contained. This resolves the contradiction by making the additional complexity localized to layer boundaries rather than requiring complex inter-layer dependencies.
Solution Approach 2:
The patent performs preliminary action by initializing the context for each layer before decoding operations begin. This preliminary initialization enables flexible decoding order selection without requiring complex runtime context management, as the necessary context state is prepared in advance for each layer, reducing overall processing complexity despite increased adaptability.
Data Source
AI summary
The present disclosure relates to an image processing apparatus and a method that enable decoding of encoded data of an octree in various processing orders. The octree corresponding to point cloud data is encoded after the context is initialized for each layer of the octree. Further, a breadth-first order or a depth-first order is selected as the decoding order for the encoded data of the octree corresponding to point cloud data, and the encoded data is decoded in the selected decoding order. The present disclosure can be applied to an image processing apparatus, an electronic apparatus, an image processing method, a program, or the like, for example.