Lossless Value-Location Encoding for Sparse Digital Signals
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Solution Overview
Problem
Existing signal processing techniques are inefficient in encoding digital signals with predominantly one sample value and sparse excursions to multiple other values, as they are optimized for sequences with a single additional sample value, leading to suboptimal compression and data handling capacity.
Innovation Solution
The method involves lossless value-location encoding, where a digital signal is decomposed into coding vectors representing the locations of distinct sample values, with a reference data value and coding order determined to optimize compression, and these vectors are encoded and transmitted, allowing for efficient representation and reconstruction of the original signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If run-length coding is used to encode digital signals with predominantly one sample value, then compression efficiency is improved when there is a single additional sample value, but compression efficiency deteriorates when there are multiple additional sample values
Solution Approach 1:
The patent segments the encoding process into multiple stages: first identifying the predominant sample value and its locations, then separately encoding the locations of non-predominant sample values. This segmentation allows the system to handle signals with multiple distinct sample values efficiently by processing them in discrete, manageable portions rather than attempting to encode all variations simultaneously.
Solution Approach 2:
The patent transitions from value-based encoding to location-based encoding. Instead of encoding the actual sample values directly, the system encodes the locations (indices) where non-predominant values occur. This dimensional transformation from value space to position space enables more efficient compression by exploiting the sparsity of non-predominant values throughout the signal.
2Productivity
If the number of bits used to represent the signal is reduced to maximize data handling capacity, then transmission and storage efficiency is improved, but signal reconstruction accuracy may deteriorate
Solution Approach 1:
The patent extracts only the essential information needed for signal reconstruction - specifically, the locations of non-predominant sample values. By removing redundant information about the predominant values (which can be easily inferred) and focusing only on the sparse non-predominant occurrences, the system achieves high compression ratios while maintaining complete signal reconstructability.
Solution Approach 2:
The patent creates a compressed representation (copy) of the signal that contains only the critical location information. This compressed copy is sufficient to perfectly reconstruct the original signal because the predominant sample values can be automatically recovered by filling in the unmarked positions, thus achieving lossless compression with reduced bit depth.
Data Source
AI summary
A method of encoding samples in a digital signal is provided that includes receiving a frame of N samples of the digital signal, determining L possible distinct data values in the N samples, determining a reference data value in the L possible distinct data values and a coding order of L−1 remaining possible distinct data values, wherein each of the L−1 remaining possible distinct data values is mapped to a position in the coding order, decomposing the N samples into L−1 coding vectors based on the coding order, wherein each coding vector identifies the locations of one of the L−1 remaining possible distinct data values in the N samples, and encoding the L−1 coding vectors.