Pixel Block Encoder Using PCA for Real-Time Joint-Color Compression
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Solution Overview
Problem
Conventional image data compression methods for artificial reality systems often result in decreased quality and perceptibility of compressed images, and existing compression techniques are too slow for real-time encoding and decoding, particularly in head-mounted displays with limited computational resources.
Innovation Solution
A modular architecture of a pixel block encoder using a ring buffer and parallel processing units, combined with Principal Component Analysis (PCA) to determine joint-color mode encoding based on pixel correlations, minimizing data movement and computational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional image data compression methods are used, then data transmission bandwidth and memory storage costs are reduced, but image quality and perceptibility decrease
Solution Approach 1:
The encoder divides the image into multiple pixel blocks and processes each block independently through parallel processing units. This segmentation allows for optimized compression of each block while maintaining overall image quality, as each block can be encoded with appropriate compression parameters based on its local characteristics
Solution Approach 2:
The system applies different compression strategies to different pixel blocks based on their importance and characteristics. Critical regions maintain higher quality with less compression, while less important regions undergo more aggressive compression, achieving overall bandwidth reduction without uniformly sacrificing image quality
2Loss of energy
If conventional compression techniques are used, then data redundancy is reduced, but encoding and decoding speed becomes too slow for real-time processing
Solution Approach 1:
The image processing task is divided into multiple pixel blocks that can be encoded simultaneously by different processing units. This parallel segmentation approach maintains comprehensive redundancy reduction while achieving real-time encoding speeds through concurrent processing of multiple blocks
Solution Approach 2:
The system performs preliminary analysis of pixel blocks to determine optimal compression parameters before actual encoding. This pre-processing step identifies regions that can be aggressively compressed versus those requiring quality preservation, enabling faster decision-making during the encoding phase and improving overall processing throughput
3Manufacturing precision
If more computational resources are allocated for encoding, then image quality is maintained, but computational costs and latency increase
Solution Approach 1:
The encoder applies full computational resources only to pixel blocks that require high quality preservation, while using simplified compression algorithms for blocks that can tolerate higher compression ratios. This selective application of computational effort maintains image quality where needed while reducing overall computational costs
Solution Approach 2:
Different compression algorithms and quality parameters are applied to different regions of the image based on local characteristics. Important regions receive high-quality encoding with more computational resources, while less important regions use more efficient compression with fewer computational resources, optimizing the trade-off between quality and cost
4Productivity
If parallel processing is implemented, then encoding speed is improved, but device complexity increases
Solution Approach 1:
The encoder architecture is segmented into multiple independent processing units, each capable of encoding a pixel block autonomously. This modular segmentation enables parallel processing and improved encoding speed while keeping each individual unit relatively simple, thus managing overall device complexity through functional decomposition
Data Source
AI summary
In an embodiment, a system includes a buffer configured to store a plurality of pixel blocks of an image, a first processor unit configured to receive a pixel block of the of the plurality of pixel blocks and select whether to separately encode or jointly encode pixel components of the pixel block by computing eigenvalues for the pixel components, a second processor unit configured to compute, responsive to the first processing unit selecting to jointly encode the pixel block, (i) an eigenvector for the pixel components of the pixel block based on the eigenvalues and (ii) endpoints on the eigenvector for encoding the pixel components, an encoder unit configured to encode, responsive to the first processing unit selecting to jointly encode the pixel block, the pixel components of the pixel block jointly based on the eigenvector and the endpoints.