Z-Lossless Depth Stream Compression via Histogram Compaction
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Solution Overview
Problem
Consumer class depth cameras generate large amounts of data quickly, posing challenges for real-time processing and storage, especially in mobile or remote applications due to bandwidth and power constraints, as existing compression methods are either lossy, non-real-time, or require substantial computational power.
Innovation Solution
A novel Z-lossless (ZLS) compression scheme that utilizes histogram compaction, a customized predictor-corrector model, and adaptive Golomb codes to achieve ultra-lightweight computational requirements and high compression ratios, enabling real-time lossless compression of depth streams on low-power devices like the ARM Cortex A9 core.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing compression methods are used for depth data, then compression ratio is improved, but real-time performance is lost due to non-real-time processing requirements
Solution Approach 1:
The patent transforms the depth data representation by changing parameters: converting signed depth values to unsigned prediction residuals, applying bitplane decomposition to reorganize data bits, and using adaptive Golomb-Rice coding parameters tailored for depth data statistics. These parameter transformations enable the system to achieve both high compression ratios and real-time processing speeds by optimizing the data representation for the specific characteristics of depth streams.
2Loss of information
If lossless compression is applied to depth streams, then data accuracy is maintained, but computational power requirements increase substantially
Solution Approach 1:
The patent extracts and exploits the specific statistical properties and characteristics of depth data to design a specialized compression system. By taking out the unique features of depth streams (such as spatial coherence, temporal redundancy, and specific value distributions) and building a tailored compression approach around them, the system achieves lossless compression with significantly reduced computational requirements compared to general-purpose lossless compressors.
Solution Approach 2:
The patent replaces complex mechanical compression processes with more efficient computational approaches: substituting general-purpose entropy coders with adaptive Golomb-Rice coding optimized for depth data, replacing intensive prediction algorithms with simpler difference-based prediction, and using bitplane operations that can be efficiently implemented in hardware or software.
3Quantity of substance
If high compression ratios are achieved for depth data, then transmission efficiency is improved, but processing complexity increases
Solution Approach 1:
The patent segments the depth data into bitplanes, separating the compression process into independent stages: prediction residual calculation, bitplane decomposition, run-length encoding of zero sequences in each bitplane, and adaptive Golomb-Rice coding. This segmentation allows each component to be optimized independently and simplifies the overall processing pipeline, making it suitable for real-time implementation while achieving high compression ratios.
Data Source
AI summary
Various examples are provided for lossless compression of data streams. In one example, a Z-lossless (ZLS) compression method includes generating compacted depth information by condensing information of a depth image and a compressed binary representation of the depth image using histogram compaction and decorrelating the compacted depth information to produce bitplane slicing of residuals by spatial prediction. In another example, an apparatus includes imaging circuitry that can capture one or more depth images and processing circuitry that can generate compacted depth information by condensing information of a captured depth image and a compressed binary representation of the captured depth image using histogram compaction; decorrelate the compacted depth information to produce bitplane slicing of residuals by spatial prediction; and generate an output stream based upon the bitplane slicing.