Flexible Huffman Tree Approximation for Low-Latency Encoding
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Solution Overview
Problem
Huffman encoding, while optimal for integer length codes, is inefficient in terms of computing resources and poses challenges for hardware implementations, particularly in achieving low latency and high-speed encoding.
Innovation Solution
A new algorithm, Quantization Interval Huffman Approximation (QuIHA), approximates Huffman coding by using phases such as Shannon-based binning, code space optimization, tree completion, and code assignment, which allows for efficient encoding with reduced computing resources and latency, suitable for hardware implementations like FPGAs and ASICs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Huffman encoding is used, then optimal compression ratio is achieved, but computing resource efficiency deteriorates and latency increases
Solution Approach 1:
The patent segments the Huffman encoding process into distinct phases: probability calculation, code generation, and encoding execution. This segmentation allows parallel processing of multiple symbols during the encoding phase, significantly improving throughput while maintaining optimal compression ratios.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating and storing Huffman codes in lookup tables during an initialization phase. This pre-computation eliminates the need for real-time tree traversal during actual encoding, reducing latency and enabling high-speed hardware implementation without sacrificing compression optimality.
2Measurement precision
If traditional Huffman encoding is used, then optimal integer length codes are achieved, but hardware implementation complexity increases
Solution Approach 1:
The patent uses lookup tables that store pre-computed Huffman codes as copies of the optimal coding scheme. Instead of implementing complex real-time tree traversal logic in hardware, the system copies the essential information (code assignments) into accessible memory structures, dramatically simplifying hardware while preserving code optimality.
Solution Approach 2:
The patent replaces the mechanical tree-traversal process with a direct memory access system. Instead of physically navigating through tree structures using comparators and switches, the system substitutes this mechanical process with electronic lookup in pre-computed tables, reducing hardware complexity and improving speed.
3Measurement precision
If Huffman encoding is used, then optimal compression is achieved, but latency increases due to computational overhead
Solution Approach 1:
The patent performs all computationally intensive operations (probability calculation, code generation) in advance during an initialization phase. The results are stored in lookup tables, allowing the actual encoding to proceed with minimal computation and latency, thus resolving the time-efficiency tradeoff.
Solution Approach 2:
The system serves itself by automatically generating and storing the Huffman codes during initialization without requiring external intervention or real-time computation. This self-service approach eliminates ongoing computational overhead during encoding operations, reducing latency while maintaining compression efficiency.
Data Source
AI summary
Techniques are described for encoding symbols using a new algorithm that provides a flexible Huffman tree approximation and that can be used for low latency encoding. For example, the new algorithm can perform encoding using one or more of the following phases: Shannon-based binning, code space optimization, tree completion, and code assignment.