Texture Data Compression Using Weighted Point Selection
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Solution Overview
Problem
Current methods for compressing texture data in volumetric three-dimensional displays result in loss of important color information, leading to reduced display accuracy and increased data processing time due to the need to traverse all texture points in the image.
Innovation Solution
A method that assigns weights to texture points based on their importance and compresses data by storing color information in a multi-tree structure, where weights and point counts are used to determine which nodes to delete, ensuring the retention of critical data points and reducing the need to process all texture points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all texture points are processed and stored to maintain high display accuracy, then the texture data quality is improved, but the data processing time and computational resources increase significantly
Solution Approach 1:
The patent applies local quality by differentiating between important and unimportant texture points through weight assignment. Different regions of the texture map receive different levels of processing priority based on their visual importance, allowing high accuracy for critical areas while reducing processing for less important areas.
Solution Approach 2:
The patent changes the parameter of texture point selection by introducing weight values that prioritize certain texture points over others. This parameter change enables selective processing where only high-weight (important) texture points are fully processed and stored, rather than uniformly processing all points.
2Quantity of substance
If all texture data is compressed to reduce data size, then the storage efficiency is improved, but the loss of color information increases and display quality deteriorates
Solution Approach 1:
The patent applies local quality by applying different compression strategies to different texture points based on their importance weights. Important texture points (high weight) undergo minimal or no compression to preserve color information, while unimportant points (low weight) are compressed more aggressively.
Solution Approach 2:
The patent extracts and prioritizes important color information by identifying and weighting key texture points that contribute most to visual quality. Only this extracted important information is preserved at full quality, while redundant information is compressed or discarded.
3Productivity
If a simple compression algorithm is used to reduce computational complexity, then the processing speed is improved, but the ability to retain important texture information deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-calculating and assigning importance weights to all texture points before compression. This preliminary classification enables the subsequent compression stage to quickly identify and preserve only important information without complex real-time analysis, maintaining both speed and quality.
Solution Approach 2:
The patent applies local quality by using different compression algorithms or parameters for different regions based on their importance weights. High-importance regions use lossless or high-quality lossy compression, while low-importance regions use more aggressive compression, optimizing the balance between speed and quality.
Data Source
AI summary
Provided is a method for compressing texture data of a three-dimensional model, including: acquiring data of polygonal surfaces constituting a contour of a target three-dimensional model, and data of a texture image and a phase plane of the contour; acquiring the texture data of texture points in a texture point group corresponding to each polygonal surface, the texture points in the texture point group including first and second texture points; assigning a weight to each texture point in each texture point group according to the texture data of the first texture points in the texture point group; calculating a sum of weights of each texture point in each texture point group; and compressing the texture data, according to a target compressed data amount, and the texture data and the sum of weights of the texture point whose sum of weights is not zero.