Depth Data Compression Using Segmented Clip Coordinate Spaces
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional compression techniques for virtual reality data compromise depth data precision and accuracy to reduce resource usage, leading to a tradeoff between quality and performance in immersive technologies like virtual reality.
Innovation Solution
The method involves dividing depth representations into sections with different depth ranges, processing each section individually, and transmitting them with metadata to enable accurate reconstruction at the media player device, using inverse view-projection transforms to maintain precision even with reduced data bits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional compression techniques are used to reduce resource usage, then data transmission efficiency is improved, but depth data precision and accuracy deteriorate
Solution Approach 1:
The depth representation is divided into multiple sections, each associated with a different clip coordinate space and inverse view-projection transform. This segmentation allows each section to be compressed independently while preserving depth precision through coordinated transformation matrices, resolving the contradiction between compression efficiency and precision maintenance.
Solution Approach 2:
The patent changes the parameter representation by introducing clip coordinate spaces with custom depth ranges for each section. By adjusting these parameter ranges and associated transformation matrices, the system achieves efficient compression while maintaining measurement precision through the inverse view-projection transforms that reconstruct accurate depth values.
2Quantity of substance
If depth data is compressed to reduce bandwidth usage, then resource consumption is reduced, but quality and detail of depth data deteriorate
Solution Approach 1:
The depth data is segmented into multiple sections with different clip coordinate spaces. Each section can be compressed to a smaller data size while the inverse view-projection transforms ensure that depth quality and detail are preserved through accurate reconstruction of depth values in the original coordinate system.
Solution Approach 2:
Inverse view-projection transforms serve as intermediary mathematical operations that bridge the compressed representation and the original high-quality depth data. These transforms act as mediators that reconstruct accurate depth information from the compressed sections, maintaining manufacturing precision while reducing data quantity.
Data Source
AI summary
An exemplary data precision preservation system divides a depth representation into a first section and a second section separate from the first section. The system determines data bits representing numbers that correspond to a lowest non-null depth value and a highest non-null depth value represented in the first section, and converts an original set of depth values represented in the first section to a compressed set of depth values normalized based on the lowest and highest non-null depth values represented in the first section. The system then generates a dataset that includes data representative of the compressed set of depth values and an inverse view-projection transform that is based on the lowest and highest non-null depth values represented in the first section and is configured to facilitate conversion of the compressed set of depth values back to the original set of depth values. Corresponding systems and methods are also disclosed.