OBHS: An Optimized Block Huffman Scheme for Real-Time Audio Compression

2025-11-15

AI Summary

To address the need for lossless compression of real-time audio streams, this paper proposes a low-complexity block-wise adaptive Huffman coding algorithm. The method integrates fixed-size block processing, block-level adaptive Huffman tree construction, canonical code representation, and an intelligent fallback mechanism, achieving linear-time encoding complexity O(n). Its key contributions are: (1) joint intra-block statistical modeling and inter-block codebook coordination to significantly improve encoding/decoding efficiency while guaranteeing lossless reconstruction; and (2) elimination of global model building overhead, thereby balancing real-time performance and compression ratio. Experimental results show that the algorithm achieves a 93.6% compression ratio on silence-rich audio and maintains consistently high compression performance across pink noise, pure-tone, and real-world recordings. On average, it outperforms conventional Huffman coding by over 12% in compression ratio, making it well-suited for resource-constrained real-time audio transmission scenarios.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Data Mining & Knowledge Management: Data Compression
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

In this paper, we introduce OBHS (Optimized Block Huffman Scheme), a novel lossless audio compression algorithm tailored for real-time streaming applications. OBHS leverages block-wise Huffman coding with canonical code representation and intelligent fallback mechanisms to achieve high compression ratios while maintaining low computational complexity. Our algorithm partitions audio data into fixed-size blocks, constructs optimal Huffman trees for each block, and employs canonical codes for efficient storage and transmission. Experimental results demonstrate that OBHS attains compression ratios of up to 93.6% for silence-rich audio and maintains competitive performance across various audio types, including pink noise, tones, and real-world recordings. With a linear time complexity of O(n) for n audio samples, OBHS effectively balances compression efficiency and computational demands, making it highly suitable for resource-constrained real-time audio streaming scenarios.

Problem

• Develops lossless audio compression for real-time streaming
• Optimizes Huffman coding with blocks and canonical representation
• Balances high compression ratios with low computational complexity

Innovation

• Block-wise Huffman coding for audio compression
• Canonical code representation for efficient storage
• Linear time complexity for real-time streaming