Rate Controlled Image Compression for AR Headsets
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Solution Overview
Problem
Existing texture compression techniques in augmented and virtual reality headsets lead to chromatic aberration due to concurrent encoding of RGB color channels, resulting in increased memory consumption, power consumption, and computational costs, while being unsuitable for alpha channels with distinct data characteristics.
Innovation Solution
The proposed solution involves encoding individual color components separately and using a compression rate control method to allocate bits differently to RGB color components based on their variance, optimizing quantization levels, and selectively allocating bits to alpha components to reduce memory consumption and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If concurrent encoding of RGB color channels is used, then compression speed is improved, but visual quality deteriorates due to chromatic aberration and quantization error
Solution Approach 1:
The patent divides the concurrent encoding process into separate sequential encoding stages for each color channel (RGB). Instead of encoding all three channels simultaneously, the system encodes them one after another, allowing independent optimization of quantization parameters for each channel based on its specific characteristics and human visual sensitivity, thereby reducing chromatic aberration and quantization error while maintaining acceptable compression speed
Solution Approach 2:
The patent applies different quantization parameters and encoding strategies to different color channels based on their local characteristics and human visual sensitivity. Each color channel is analyzed individually and assigned appropriate quantization levels, allowing the system to allocate more precision to channels where humans are more sensitive and less precision to channels where it matters less, thus improving overall visual quality without uniformly increasing compression cost
2Device complexity
If uniform bit allocation is used for all color components, then encoding simplicity is improved, but visual quality deteriorates due to unequal color information content
Solution Approach 1:
The patent implements non-uniform bit allocation across different color channels by analyzing the variance and information content of each channel separately. Channels with higher variance or greater visual importance receive more bits, while channels with lower variance receive fewer bits. This localized optimization improves visual quality by matching bit allocation to actual channel characteristics rather than applying a uniform standard
Solution Approach 2:
The patent dynamically adjusts quantization parameters and bit allocation based on the statistical properties of each color channel. By calculating variance metrics and human visual sensitivity factors for each channel, the system modifies encoding parameters adaptively, allocating more bits to channels requiring higher precision and fewer bits to channels tolerating coarser quantization, thus optimizing the quality-bitrate tradeoff
3Manufacturing precision
If separate encoding of individual color components is used, then visual quality is improved by minimizing quantization error, but memory consumption and computational cost increase
Solution Approach 1:
The patent processes and stores color channel data in segmented, channel-specific buffers rather than requiring full-resolution storage of all channels simultaneously. By dividing the encoding workflow into separate stages for each color channel and using incremental compression techniques, the system reduces peak memory requirements while still allowing independent optimization of each channel's quantization parameters for improved visual quality
Solution Approach 2:
The patent applies partial precision to different color channels based on their actual information content and visual importance. Instead of using full precision for all channels uniformly, the system applies higher precision only where necessary (channels with high variance or visual sensitivity) and lower precision where it can be tolerated, thereby reducing overall memory consumption and computational cost while maintaining visual quality in critical areas
4Loss of substance
If existing texture compression techniques are used, then compression ratio is improved, but chromatic aberration occurs due to lens focal length differences
Solution Approach 1:
The patent separates the color channels into independent encoding streams, allowing each channel to be compressed and processed individually. This segmentation prevents the mixing of chromatically shifted data that occurs in concurrent encoding, enabling the system to maintain high compression ratios while avoiding chromatic aberration by keeping color channel data separate throughout the compression pipeline
Solution Approach 2:
The patent applies channel-specific quantization and compression parameters tailored to each color channel's characteristics and the specific chromatic aberration profile of the display system. By analyzing and compensating for focal length differences on a per-channel basis, the system optimizes compression ratios while minimizing chromatic aberration artifacts in the final rendered image
Data Source
AI summary
In one embodiment, a computing system may access color components of a pixel region in an image, and then determine a color variance for each of the color components. The computing system may further determine a desired bit allocation for each of the color components based on the color variance associated with that color component. The computing system may then determine a total bit allocation for the pixel region based on the desired bit allocations for the color components, as well as a number of unallocated bits available for allocation. The computing system may further determine a final bit allocation for each of the color components by allocating the total bit allocation to each of the color components according to the desired bit allocation for each of the color components. The computing system may then encode each of the color components using the associated final bit allocation.