Binary Image Encoding With Run-Length and Differential Encoding
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Solution Overview
Problem
Generating and transmitting high-resolution binary images, such as cutout masks, for head-mounted devices (HMDs) in mixed reality systems is challenging due to computational limitations, requiring high network bandwidth, and achieving low-latency, high-compression ratios is difficult with existing encoding techniques.
Innovation Solution
A combination of run-length, differential, variable length encoding, and lossless compression techniques is applied to binary images, enabling lossless encoding and decoding with a compression ratio of at least 100:1 and computational speed of one millisecond.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution binary images are transmitted via network, then image accuracy is improved, but network bandwidth requirement increases
Solution Approach 1:
The binary image is divided into multiple rows, and each row is independently processed through run-length encoding to create compressed data. This segmentation allows efficient compression while maintaining the ability to reconstruct the full high-resolution image accurately.
Solution Approach 2:
The patent applies multiple encoding techniques including run-length encoding, differential encoding, and variable length encoding to transform the binary image data into a more compact representation. These parameter changes reduce the data size significantly while preserving image accuracy.
2Quantity of substance
If compression techniques are applied to reduce bandwidth, then network bandwidth is reduced, but compression and decompression time increases
Solution Approach 1:
The patent performs preliminary encoding actions by applying run-length encoding and differential encoding to the binary image data before final compression. This preliminary processing prepares the data in a way that enables faster decompression while achieving high compression ratios.
Solution Approach 2:
The patent uses variable length encoding that dynamically adapts to the data characteristics, allowing efficient compression without excessive computational overhead. The encoding process adjusts its behavior based on the input data patterns to optimize both compression ratio and processing speed.
3Measurement precision
If powerful remote renderer is used to generate cutout mask, then mask quality is improved, but device complexity and cost increases
Solution Approach 1:
The patent creates a compressed representation (copy) of the binary image data that can be transmitted to less powerful devices. The compressed format preserves the essential information needed for mask generation, allowing lower-power devices to produce acceptable results without requiring the full computational power of a remote renderer.
Solution Approach 2:
The compressed encoded data acts as an intermediary between the high-quality mask generation process and the display device. This intermediary format enables efficient transmission and processing, bridging the gap between computational requirements and device capabilities.
Data Source
AI summary
In various examples there is a method for encoding a binary image, the method comprising receiving the binary image; performing run-length encoding on pixel data of the binary image to produce run-length encoded data; performing differential encoding on the run-length encoded data to produce differential encoded data; performing variable length encoding on the differential encoded data to produce variable length encoded data; and applying a lossless compressor to the variable length encoded data to produce compressed data, the compressed data being an encoded binary image.