RAHT Point Cloud Coding with Cross-Attribute Prediction

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Solution Overview

Problem

Existing point cloud coding technologies neglect cross-attribute and cross-component correlations during color attribute compression, leading to inefficient encoding and decoding processes.

Innovation Solution

Implement cross-component and cross-attribute prediction methods, such as predicting a first signal attribute (Y) from a second signal attribute (X) using linear or non-linear models, and applying these predictions in the transform domain before or during region-adaptive hierarchical transform (RAHT) coding, with model parameters derived or signaled based on distance, neighborhood, and layer-specific criteria.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If cross-attribute and cross-component prediction methods are implemented, then coding efficiency is enhanced and bitrate is reduced, but device complexity and processing overhead increase

Engineering Contradiction:

Improvecoding efficiencyVSAvoidprocessing complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent applies preliminary action by performing cross-attribute and cross-component prediction before the transform domain coding. Prediction models are established and parameters are derived in advance using correlation analysis between attributes (e.g., between R, G, B color components or between different geometric attributes). This preliminary prediction step allows the encoder to exploit correlations and reduce the amount of data requiring transform coding, thereby improving coding efficiency while managing complexity through structured preprocessing

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements parameter changes by dynamically adjusting prediction model parameters based on local characteristics of the point cloud data. Different prediction models (linear, non-linear) and their parameters are selected and adapted according to the specific attributes being coded, the spatial location, and the correlation strength between attributes. This allows the system to optimize coding efficiency for different regions and attribute types while maintaining manageable complexity through adaptive parameter selection

Inventive Principle:

Principle #35Parameter changes

2Loss of information

If cross-attribute and cross-component prediction is applied, then bitrate is reduced through correlation exploitation, but encoding and decoding time increases

Engineering Contradiction:

ImprovebitrateVSAvoidprocessing time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The patent applies partial action by selectively enabling cross-attribute and cross-component prediction only for attribute pairs or regions where correlation analysis indicates significant benefit. Rather than applying prediction uniformly to all attributes and regions, the system performs correlation assessment and applies prediction models only where they will effectively reduce bitrate. This selective approach reduces unnecessary processing overhead while maintaining bitrate compression benefits in relevant areas

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20260032287A1Cross-component and cross-attribute prediction for region-adaptive hierarchical transform in point cloud coding

Publication Date: 2026.01.29 DOUYIN VISION CO LTD

AI summary

A mechanism for processing video data is disclosed. The mechanism can include determining a first signal (Y) attribute can be predicted from a second signal (X) attribute. A conversion can then be performed between a visual media data and a bitstream based on the Y attribute and the X attribute.