Fixed-Length Block Compression for Parallel Decoding
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Solution Overview
Problem
Existing data compression techniques, such as Huffman encoding, are computationally costly and inefficient due to their reliance on variable-length codes, which hinder parallel decoding and increase processing overhead.
Innovation Solution
A method involving a data storage controller that performs frequency analysis on equal-sized data blocks, encoding frequently occurring blocks with unique fixed-length codes and infrequently occurring blocks with a common code, organized in a structured format for efficient compression and decompression, allowing for parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If variable-length encoding (Huffman encoding) is used to compress data, then data size is reduced, but decoding cannot be performed in parallel and processing efficiency deteriorates
Solution Approach 1:
The patent segments the compressed data stream into fixed-length code units (e.g., 8-bit bytes). Each code unit in the compressed data corresponds to a fixed number of original data elements, enabling parallel processing. The compression structure divides data into compressible portions (with repeated patterns) and non-compressible portions, organized in fixed-size blocks that can be independently processed during decompression.
Solution Approach 2:
The patent changes the encoding parameter from variable-length codes to fixed-length codes. Instead of using Huffman codes of varying lengths, the system uses uniform fixed-length code units (e.g., one byte per code unit). This parameter change enables parallel decoding while maintaining compression through the fixed structure that allows multiple code units to be processed simultaneously.
2Quantity of substance
If variable-length codes are used for compression, then data representation is optimized, but computational cost increases
Solution Approach 1:
The patent uses simple, fixed-length code units (cheap objects) instead of complex variable-length Huffman codes. Each code unit is a straightforward fixed-length value that requires minimal computational resources to encode and decode. The simplicity of fixed-length encoding makes the compression operation computationally inexpensive while still achieving data reduction through the fixed structure.
3Productivity
If fixed-length compression codes are used for frequently occurring blocks, then decoding can be performed in parallel, but compression ratio may be limited
Solution Approach 1:
The patent applies different encoding strategies to different portions of the data based on local characteristics. Frequently occurring blocks or patterns are encoded with fixed-length compression codes that enable parallel processing, while less frequent or non-repeated data is handled differently. This local differentiation optimizes both compression efficiency and decoding speed by matching the encoding method to the data characteristics in each region.
Data Source
AI summary
Systems and methods for performing compression of data. A data buffer is separated into equal-sized segments of data. A frequency count is performed to determine how often each segment of data appears in the data buffer. Frequently occurring segments are encoded with unique compression codes, while all other infrequently occurring segments are encoded with a common compression code. The compressed data buffer includes the compression codes, which are all of the same bit-length, and the uncompressed segments. The compression codes and the uncompressed segments are stored in the compressed data buffer in the order in which the corresponding segments appear in the original data buffer.