ANS Decoding Table for Hardware-Friendly Data Decompression
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Solution Overview
Problem
Existing data compression techniques, such as those using Asymmetric Numeral Systems (ANS), are not optimized for hardware-friendly implementations like Field Programmable Gate Arrays (FPGA) or Application Specific Integrated Circuits (ASIC), requiring a more efficient lossless compression method suitable for these platforms.
Innovation Solution
The method involves entropy encoding with a decoding table constructed from distinct symbols, their occurrences, and dynamic bit recovery, allowing for efficient decoding and hardware-friendly implementation by reducing the number of encoding states and storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If general-purpose ANS implementations are used, then compression ratio is improved, but hardware implementation complexity increases
Solution Approach 1:
The patent segments the decoding process into distinct phases: obtaining occurrence counts for each symbol, constructing a decoding table with specific columns (symbol, cumulative occurrence, bit recovery function, state transition function), and executing decoding using this pre-constructed table. This segmentation transforms the complex general-purpose ANS algorithm into structured, hardware-friendly modules that can be efficiently implemented in FPGA or ASIC.
2Measurement precision
If detailed decoding tables with all state information are constructed, then decoding accuracy is improved, but storage requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-construcing the decoding table during an initialization phase before actual decompression begins. The table includes pre-calculated cumulative occurrence values, bit recovery functions, and state transition functions for all possible symbols and states. This pre-computation eliminates the need for complex runtime calculations, improving decoding speed and accuracy while the structured format optimizes storage efficiency for hardware implementation.
Data Source
AI summary
Systems, apparatus and methods are provided for decompressing compressed data. A method may include receiving encoded data to be decompressed, obtaining a “Stotal” of a total number of symbols, numbers of occurrences for distinct symbols in the encoded data, and a final state generated during an encoding process as a first state for decoding, building a decoding table containing a row of the distinct symbols, a row of substitutes for numbers of bits to be recovered and a row of substitutes for new states corresponding to encoding states, decoding using the decoding table including: obtaining a current symbol from the decoding table based on a current state X, dynamically determining a current number of bits to be recovered from the encoded data and a new state X based on corresponding substitutes and outputting symbols recovered. L may be a sum of the numbers of occurrences for the distinct symbols.