Point Cloud Attribute Decoding With Logarithmic Parameter Coding
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Solution Overview
Problem
Current point cloud coding methods, such as geometry point cloud coding (G-PCC), are not effective for a wide range of inputs and applications, necessitating improved methods and systems for encoding and decoding point cloud data.
Innovation Solution
The proposed solution involves encoding and decoding point cloud data using a logarithmic format for specific parameters, such as maxNumofCoeff, colorGolumbNum, and RefGolombNum, with constraints to ensure compatibility and efficient hardware implementation, including the use of exponential Golomb coding and context-based arithmetic coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current G-PCC methods are used for point cloud coding, then the coding process is simple, but the effectiveness is limited for a wide range of inputs and applications
Solution Approach 1:
The patent implements dynamic adaptation by allowing the encoder to select different coding modes (first coding mode for general cases, second coding mode for specific cases) based on the input characteristics. The decoder similarly adapts by determining which mode was used and applying the corresponding decoding process, enabling the system to adjust its behavior dynamically to match diverse point cloud inputs while maintaining a unified coding framework.
Solution Approach 2:
The patent changes key parameters in the bitstream syntax to enable different coding modes. Specifically, it introduces a `codingMode` parameter that can take different values (e.g., 0 for first coding mode, 1 for second coding mode), and modifies parameters like `log2maxNumofCoeffMinus8` and attribute coding parameters (`colorGolumbNum`, `RefGolombNum`) accordingly. This parameter-based approach allows the same coding framework to handle various input types effectively.
2Reliability
If logarithmic format with fixed integer is used for encoding parameters, then compatibility with standards like AVS-GPCC is improved, but the decoding process becomes more complex
Solution Approach 1:
The patent applies preliminary action by pre-defining the logarithmic format with fixed integer offsets in the encoding stage. The encoder explicitly calculates and encodes parameters such as `log2maxNumofCoeffMinus8` with the fixed value 8 already accounted for. This preliminary preparation ensures that when the decoder receives the bitstream, it can directly apply the corresponding reverse operation (adding the fixed integer) without needing to parse complex formatting rules, thus maintaining compatibility while simplifying the actual decoding execution.
3Productivity
If exponential Golomb coding and context-based arithmetic coding are used, then data compression efficiency is improved, but the implementation complexity increases
Solution Approach 1:
The patent segments the attribute coding process into distinct parts: geometry coding using exponential Golomb coding for certain parameters, and attribute coding using context-based arithmetic coding for color and reflectance attributes. This segmentation allows each coding method to be applied where it is most effective, with exponential Golomb providing efficient coding for geometric parameters and context-based arithmetic coding optimizing attribute data, while keeping the overall implementation manageable through modular design.
Data Source
AI summary
A method of encoding point cloud data, a method of decoding point cloud data, and a point cloud decoder are disclosed. The method of decoding point cloud data includes decoding a geometry information of a point cloud data and decoding an attribute information of the point cloud data based on the geometry information, wherein a first parameter associated with the attribute information is decoded with a Logarithmic format plus a fixed integer, or at least one value associated with the attribute information is specified.