3D Mesh Vertex Compression Using Quantization and De-Interleaving
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Solution Overview
Problem
Existing image compression techniques for 3D computer graphics models face challenges in balancing high compression ratios with efficient memory usage and visual accuracy, particularly in storing vertex attributes of meshes.
Innovation Solution
Implementing lossy and lossless pre-processing methods to enhance compression ratios by quantizing and de-interleaving vertex attribute vectors before compression, using techniques such as Lempel-Ziv-Welch-based compressors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lossy pre-processing (quantizing) is applied to vertex attribute vectors, then compression ratio is improved, but manufacturing precision (visual accuracy) deteriorates
Solution Approach 1:
The patent applies quantization to vertex attribute vectors, transforming continuous floating-point values into discrete integer values by changing the precision parameter. This parameter change enables higher compression ratios while controlling visual accuracy degradation through selective precision reduction in less visually sensitive attribute components.
Solution Approach 2:
The patent differentiates between various vertex attribute types (position, texture coordinates, normals) and applies different quantization strategies to each. Position attributes may use lower precision while texture coordinates maintain higher precision, as different attributes have different visual sensitivity. This local quality approach optimizes the balance between compression and visual fidelity for each attribute type.
2Quantity of substance
If higher compression ratios are used, then storage space is improved, but cycle-intensive compression procedures increase power consumption
Solution Approach 1:
The patent performs pre-processing (quantization and de-interleaving) on vertex attribute data before compression. This preliminary action transforms the data into a more compressible format, enabling higher compression ratios to be achieved with less intensive compression algorithms, thereby reducing power consumption during the compression phase while still attaining significant storage space reduction.
3Quantity of substance
If vertex attributes are stored in compressed format, then storage efficiency is improved, but decoding complexity increases
Solution Approach 1:
The patent de-interleaves vertex attribute data by separating different attribute types (positions, texture coordinates, normals) into distinct streams or blocks. This segmentation organizes the compressed data structure so that decoding can process each attribute type independently, reducing overall decoding complexity while maintaining high storage efficiency through the compressed representation.
Data Source
AI summary
Methods for lossy and lossless pre-processing of image data. In one embodiment, a method for lossy pre-processing image data, where the method may include, at a computing device: receiving the image data, where the image data includes a model having a mesh, the mesh includes vertices defining a surface, the vertices including attribute vectors, and the attribute vectors including values. The method also including quantizing the values of the attribute vectors to produce modified values, where a precision of the modified values is determined based on a largest power determined using a largest exponent of the values, encoding pairs of the modified values into two corresponding units of information. The method also including, for each pair of the pairs of the modified values, serially storing the two corresponding units of information as a data stream into a buffer, and compressing the data stream in the buffer.