3D Point Cloud Encoding Layered Quantization

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing methods for encoding three-dimensional data, particularly point cloud data, face challenges in efficiently compressing and transmitting large amounts of data while maintaining accuracy and quality.

Innovation Solution

The proposed solution involves calculating coefficient values from attribute information of three-dimensional points, quantizing these values to generate quantization values, and creating a bitstream that includes these quantization values. This process is optimized by assigning quantization parameters to layers or groups of points, allowing for adaptive encoding based on the specific characteristics of the data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If point cloud data is compressed to reduce data amount, then transmission efficiency is improved, but encoding complexity increases

Engineering Contradiction:

Improvetransmission efficiencyVSAvoidencoding complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The point cloud data is divided into multiple layers based on depth information, with each layer containing points at similar distances from the camera. This segmentation allows independent processing of each layer, reducing overall encoding complexity while improving transmission efficiency through selective compression.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Different quantization parameters are assigned to different layers based on their importance. Foreground layers (closer to camera) use finer quantization to preserve quality, while background layers use coarser quantization to reduce data amount, achieving optimal balance between compression ratio and visual quality.

Inventive Principle:

Principle #3Local quality

2Quantity of substance

If quantization parameter is adjusted to improve compression ratio, then data amount is reduced, but decoding accuracy deteriorates

Engineering Contradiction:

Improvedata amountVSAvoiddecoding accuracy

Core Design Contradiction:

Quantity of substanceVSMeasurement precision

Solution Approach 1:

The patent dynamically adjusts quantization parameters for each layer based on depth information and importance metrics. By changing the quantization parameter values appropriately for different layers, the system achieves high compression ratios for less important data while maintaining decoding accuracy for critical foreground elements.

Inventive Principle:

Principle #35Parameter changes

3Device complexity

If uniform quantization parameter is applied to all layers, then encoding process is simplified, but encoding efficiency deteriorates

Engineering Contradiction:

Improveencoding process complexityVSAvoidencoding efficiency

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

Instead of applying a uniform quantization parameter, the patent assigns different quantization parameters to different layers based on their depth and importance. This local differentiation improves encoding efficiency by optimizing compression for each layer's characteristics while maintaining manageable encoding complexity through systematic parameter assignment.

Inventive Principle:

Principle #3Local quality

4Measurement precision

If fine quantization is applied to maintain quality, then decoding accuracy is improved, but data amount increases

Engineering Contradiction:

Improvedecoding accuracyVSAvoiddata amount

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent applies fine quantization parameters only to foreground layers where high decoding accuracy is critical, while using coarser quantization parameters for background layers. This selective parameter adjustment maintains decoding accuracy for important elements while reducing overall data amount through optimized compression of less critical data.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250045969A1Three-dimensional data encoding method, three-dimensional data decoding method, three-dimensional data encoding device, and three-dimensional data decoding device

Publication Date: 2025.02.06 PANASONIC INTELLECTUAL PROPERTY CORP OF AMERICA

AI summary

A three-dimensional data encoding method includes: calculating coefficient values from pieces of attribute information of three-dimensional points included in point cloud data; quantizing the coefficient values to generate quantization values; and generating a bitstream including the quantization values. The three-dimensional points corresponding to the coefficient values belong to one layer among one or more layers. Each of a predetermined number of layers among the one or more layers is assigned a quantization parameter for the layer. In the quantizing, (i) when a quantization parameter is assigned to a layer to which each of the coefficient values belongs, the coefficient value is quantized using the quantization parameter, and (ii) when the quantization parameter is not assigned to a layer to which each of the coefficient values belongs, the coefficient value is quantized using a quantization parameter assigned to one layer among the predetermined number of the layers.