Adaptive Range Packing Compression for VR Latency Reduction
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Solution Overview
Problem
Artificial reality systems face challenges in rendering high-quality, immersive graphics due to computational intensity and latency issues, particularly in head-mounted displays, which can lead to user discomfort and virtual reality sickness.
Innovation Solution
The implementation of image compression techniques using Principal Component Analysis (PCA) for selective encoding of pixel blocks based on color correlation, combined with adaptive range packing, to reduce processing load and latency in graphics rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional compression techniques are used, then compression is achieved, but encoding and decoding speed is too slow for real-time applications
Solution Approach 1:
The image is divided into multiple pixel blocks, and each block is independently processed using PCA-based compression. This segmentation allows parallel processing of different blocks, significantly increasing encoding and decoding speed while maintaining real-time performance requirements for artificial reality applications.
Solution Approach 2:
The patent applies Principal Component Analysis to transform the color space parameters of pixel blocks, identifying dominant eigenvectors that capture the essential color variations. By changing the representation parameters from individual pixel colors to compressed eigenvector-based representations, the system achieves faster compression while preserving visual quality.
2Manufacturing precision
If high-quality graphics are rendered, then immersion quality is improved, but computational intensity increases
Solution Approach 1:
The patent extracts and processes only the most visually important components of pixel blocks using PCA - specifically the dominant eigenvectors that capture the primary color variations. By taking out and compressing only these essential features rather than processing all pixel data, the system maintains high graphics quality while significantly reducing computational intensity and power requirements.
Solution Approach 2:
The system transforms the representation parameters of pixel data from full-color-space individual pixel values to a compressed set of eigenvector-based parameters. This parameter transformation reduces the dimensional space that needs to be processed, thereby lowering computational intensity while preserving the visual information necessary for high-quality immersive graphics.
3Ease of operation
If latency is reduced, then user comfort is improved, but processing speed must increase
Solution Approach 1:
By segmenting the image into independent pixel blocks that can be processed in parallel, the system achieves higher processing speeds without increasing sequential processing time. This parallel processing capability reduces overall latency, improving user comfort in artificial reality applications where responsive rendering is critical.
Solution Approach 2:
The patent performs preliminary PCA analysis on pixel blocks to identify dominant eigenvectors and establish compression parameters before actual rendering. This preliminary processing prepares the data in an optimized format that enables faster real-time encoding and decoding, thereby reducing latency and improving user comfort during interactive artificial reality experiences.
Data Source
AI summary
In an embodiment, a method involves receiving a pixel array, compressing the pixel array by, for each pixel block of multiple pixel blocks: accessing pixel values associated with pixels in the pixel block, determining a range of the pixel values and an endpoint pixel value in the range, determining quantization levels corresponding to different values within the range of the pixel values, selecting a quantization level from the quantization levels for each of the pixel values in the pixel block, and encoding the pixel values in the pixel block using their respective selected quantization levels and the endpoint pixel value.