JT CAD File Compression with Segmented Streaming Access
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Solution Overview
Problem
Current data compression methods for CAD files, such as the JT format, fail to minimize file size effectively while maintaining visualization performance, leading to increased storage needs and decreased application efficiency.
Innovation Solution
A hybrid compression approach is employed, using lossy compression for 3D geometry data and dictionary-based lossless compression for precise Brep data, with metadata and scene graph data compressed as single blocks, and organized into uniquely identified segments for efficient streaming access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If lossless compression is used for all CAD data, then data precision is maintained, but file size reduction is insufficient
Solution Approach 1:
The patent applies different compression strategies to different data types within the CAD file. Lossless compression is used for precise Brep data and metadata where accuracy is critical, while lossy compression is applied to 3D geometry data where approximate visual representation is sufficient. This localized application of compression quality resolves the contradiction by maintaining precision where needed while achieving file size reduction where acceptable.
Solution Approach 2:
The patent creates a composite compression approach by combining multiple compression methods (lossless and lossy) within a single file format. The hybrid compression scheme allows the system to leverage the strengths of both approaches: lossless compression for data requiring exact reproduction and lossy compression for data where visual fidelity is sufficient but space efficiency is prioritized.
2Quantity of substance
If aggressive compression is applied to minimize file size, then storage requirements are reduced, but visualization performance may degrade
Solution Approach 1:
The patent maintains visualization performance by applying lossless compression to specific data structures that are critical for accurate rendering, such as Brep data and metadata. Meanwhile, lossy compression is applied to 3D geometry data with controlled parameters that preserve visual quality while achieving significant compression. This selective approach ensures that visualization performance is maintained where it matters most.
3Quantity of substance
If data is compressed as a single block, then compression efficiency is maximized, but streaming and selective data access become difficult
Solution Approach 1:
The patent divides the compressed CAD data into distinct segments or blocks, each containing specific types of data (e.g., 3D geometry, Brep data, metadata). Each segment can be independently compressed and decompressed, enabling selective access and streaming capabilities. This segmentation resolves the contradiction by allowing the system to achieve good compression efficiency within each segment while maintaining the ability to stream and access specific segments as needed.
4Quantity of substance
If file size is reduced through compression, then storage and network utilization improve, but decompression time may increase
Solution Approach 1:
The patent segments compressed data into smaller, independently decompressible units. This allows the system to decompress only the necessary segments rather than the entire file, significantly reducing decompression time when full decompression is not required. The segmentation strategy resolves the contradiction by enabling faster partial decompression while maintaining overall compression efficiency.
Data Source
AI summary
The size of lightweight JT data files containing CAD data is reduced by employing lossy compression where acceptable for portions of the CAD data, such as 3D geometry data. Dictionary-based lossless compression for the remaining portions is augmented by exploiting common repeated structures for some portions, such as precise Brep data, and compressing separate but similar data, such as all metadata for a given part and all scene graph data, together as a single block. The compressed data is then written in separate, uniquely identified data segments indexed in a table of contents, allowing quick access to any data segment for streaming.