Block Floating-Point Compression for Variable-Range Signal Data
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Solution Overview
Problem
Existing signal compression methods fail to efficiently manage dynamic range and bit representation for signal samples, particularly in applications where signal amplitudes fluctuate over time, leading to suboptimal resource utilization and quality metrics.
Innovation Solution
The method involves grouping signal samples into blocks, determining a common exponent and adjusting the number of bits for each mantissa based on the block exponent, and encoding these values to form compressed data, allowing for flexible output data rate or quality metric control during compression and decompression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fixed-point or floating-point compression is used, then signal accuracy is maintained, but data rate and storage requirements increase
Solution Approach 1:
The patent segments the signal data into blocks of consecutive samples and applies block floating-point representation to each block. This segmentation allows the system to maintain signal accuracy within each block while reducing the overall data rate by sharing exponent information across multiple samples in the block.
Solution Approach 2:
The patent changes the representation parameters by using block floating-point format instead of traditional fixed-point or sample-by-sample floating-point. This parameter change involves determining a common exponent for each block and representing multiple samples with shared exponent information, thereby reducing the bits required per sample while maintaining accuracy.
2Adaptability or versatility
If block floating point representation is used, then dynamic range is increased, but computational complexity increases
Solution Approach 1:
The patent merges the exponent representation across multiple samples by determining a single common exponent for each block of samples. This merging approach increases the dynamic range that can be represented while reducing computational complexity compared to determining separate exponents for each sample, as the same exponent is applied to all samples in the block.
3Quantity of substance
If compression is applied to reduce storage requirements, then bandwidth is reduced, but processing time increases
Solution Approach 1:
The patent performs preliminary grouping of signal samples into blocks and determines common exponents for each block during the compression phase. This preliminary action organizes the data in a way that facilitates efficient storage and enables faster decompression later, as the block structure and shared exponent information are established upfront rather than computed during decompression.
Data Source
AI summary
A method and apparatus for compressing signal samples uses block floating point representations where the number of bits per mantissa is determined by the maximum magnitude sample in the group. The compressor defines groups of signal samples having a fixed number of samples per group. The maximum magnitude sample in the group determines an exponent value corresponding to the number of bits for representing the maximum sample value. The exponent values are encoded to form exponent tokens. Exponent differences between consecutive exponent values may be encoded individually or jointly. The samples in the group are mapped to corresponding mantissas, each mantissa having a number of bits based on the exponent value. Removing LSBs depending on the exponent value produces mantissas having fewer bits. Feedback control monitors the compressed bit rate and/or a quality metric. This abstract does not limit the scope of the invention as described in the claims.