G-PCC Quantization Parameter Scaling for Point Cloud Compression

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Solution Overview

Problem

Existing point cloud compression techniques face challenges in efficiently determining final quantization parameter (QP) values, leading to increased signaling overhead due to the need for large node QP offset terms, which affects coding efficiency.

Innovation Solution

The method involves determining final QP values as a function of a node QP offset multiplied by a geometry QP multiplier, reducing the number of bits required to signal node QP offset terms and thereby decreasing signaling overhead.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If traditional QP determination methods are used, then coding accuracy is maintained, but signaling overhead increases due to large node QP offset terms

Engineering Contradiction:

Improvesignaling overheadVSAvoidQP value precision

Core Design Contradiction:

Loss of informationVSMeasurement precision

Solution Approach 1:

The patent transforms the QP determination from using absolute node QP offset terms to using a multiplicative relationship between a base QP and a geometry QP multiplier. This parameter transformation allows the same precision to be achieved with smaller signaled values, reducing overhead while maintaining accuracy.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

Instead of signaling the final QP value directly or using large offset terms added to a base QP, the patent inverts the approach by signaling a multiplier that scales the base QP. This inversion allows the same information to be conveyed with fewer bits.

Inventive Principle:

Principle #13The other way round (Inversion)

2Measurement precision

If more bits are used to signal node QP offset terms, then QP determination accuracy improves, but coding efficiency decreases

Engineering Contradiction:

ImproveQP determination accuracyVSAvoidcoding efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

By changing from additive offset parameters to multiplicative scaling parameters, the patent achieves the same determination accuracy with compact signaled values. The geometry QP multiplier uses fewer bits while maintaining the precision needed for accurate QP determination, thereby improving coding efficiency.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If large node QP offset terms are signaled, then final QP value range is expanded, but number of bits required increases

Engineering Contradiction:

ImproveQP value rangeVSAvoidnumber of bits

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The patent introduces a new dimensional relationship by using multiplication instead of addition. The geometry QP multiplier operates in a multiplicative dimension, allowing a wide QP value range to be achieved through scaling rather than through large additive offsets, thus reducing the number of bits needed.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS11869220B2Scaling of quantization parameter values in geometry-based point cloud compression (G-PCC)

Publication Date: 2024.01.09 QUALCOMM INC

AI summary

A G-PCC coder is configured to receive the point cloud data, determine a final quantization parameter (QP) value for the point cloud data as a function of a node QP offset multiplied by a geometry QP multiplier, and code the point cloud data using the final QP value to create an coded point cloud.