Programmable Huffman Decoding Tables for Faster, Smaller Hardware
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Solution Overview
Problem
Huffman compression methods require significant computational resources and memory due to the need for software-based comparison of bit strings with pre-generated tables, making them inefficient for hardware implementation across different applications with varying Huffman tables.
Innovation Solution
A programmable decoding apparatus and method that includes a table storage unit, receiving unit, symbol extraction unit, and table generation unit to minimize table size by arranging codes, separating prefixes and suffixes, and calculating optimal suffix lengths, allowing for efficient storage and processing of Huffman codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Huffman compression is implemented using software with pre-generated tables, then decoding can be performed, but the amount of calculation increases considerably and processing speed decreases
Solution Approach 1:
The patent segments the Huffman decoding process into distinct functional units: a table generation unit that creates decoding tables, a table storage unit that holds the tables, and a symbol extraction unit that performs decoding. This segmentation allows each unit to be optimized independently and enables hardware implementation, resolving the contradiction between decoding speed and calculation complexity.
Solution Approach 2:
The patent replaces the software-based mechanical comparison process with a hardware-based system that uses pre-generated decoding tables stored in dedicated memory. The symbol extraction unit directly maps bit strings to symbols using the stored tables, eliminating the need for complex software calculations and significantly improving decoding speed while reducing computational complexity.
2Productivity
If Huffman tables are stored in memory for hardware processing, then high-speed processing is achieved, but a large capacity memory is necessary
Solution Approach 1:
The patent applies preliminary action by generating Huffman decoding tables in advance and storing them in the table storage unit before actual decoding operations. The table generation unit creates optimized tables that are then reused for multiple decoding operations, eliminating the need to recalculate during processing. This approach enables high-speed processing while managing memory capacity efficiently through pre-computation.
3Reliability
If different Huffman tables are used for different applications, then accurate decoding is achieved, but the hardware becomes inapplicable when applications change
Solution Approach 1:
The patent implements dynamics by making the table generation unit configurable to create different Huffman decoding tables based on application requirements. The system can dynamically generate and switch between tables for different applications (e.g., MPEG2, MPEG4, H.264/AVC) while maintaining the same hardware architecture. This resolves the contradiction by allowing the hardware to adapt to different applications through reconfigurable table generation rather than requiring dedicated hardware for each application.
Solution Approach 2:
The patent achieves universality by designing a single hardware decoding system that can handle multiple applications through the table generation unit. This unit can generate Huffman tables for various standards (MPEG2, MPEG4, H.264/AVC, etc.), making the same hardware applicable across different applications. The system maintains decoding accuracy for each application while providing broad versatility, eliminating the need for application-specific hardware.
4Productivity
If bit strings are processed in groups, then processing efficiency improves, but comparative calculation must be performed in units of a group and more memory is required
Solution Approach 1:
The patent segments the bit string processing into fixed-size groups that match the structure of the pre-generated decoding tables. The symbol extraction unit processes these segmented groups by direct table lookup rather than sequential comparison. This segmentation approach improves processing efficiency while keeping calculation complexity low, as each segment is independently and quickly resolved using the stored tables without requiring complex group-level comparative calculations.
Data Source
AI summary
A decoding apparatus and method store at least one table including at least one code, receive at least one instruction signal, and extract a symbol value and a symbol length from the at least one table based on the at least one instruction signal. The decoding apparatus calculates a target suffix length that minimizes the size of a generated table and minimizes the size of a non-prefix length of the at least one code.