A Temporally Adaptive Layered Image Coding Technique Based on the 3D SBC for ATM Networks

Kim, Yong Kwan; Kim, Rin Chul; Lee, Sang Uk

doi:10.1006/jvci.1998.0385

Cited by 1 publication

(1 citation statement)

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“…Using arithmetic coding and the DCT, J. [3]. The suggested technique produced large compression ratios with good image reconstructed quality.…”

Section: Review Of Related Literaturementioning

confidence: 99%

Huffman Algorithm Valuation Using Residue Number System

Dauda¹,

B²,

Isiaka³

2023

IJCATR

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The Huffman algorithm is a widely used method for lossless data compression, which assigns variable-length codes to characters based on their frequency of occurrence in the input data. However, the traditional implementation of Huffman coding using binary arithmetic can be computationally intensive, particularly for large data sets. In recent years, the Residue Number System (RNS) has emerged as a promising alternative to binary arithmetic for certain types of computations, due to its potential for parallel processing and reduced hardware complexity. This paper evaluates the use of RNS as a basis for implementing the Huffman algorithm, comparing its performance with the traditional binary approach. The results demonstrate that RNS-based Huffman coding can achieve comparable or superior compression ratios, while reducing the computational requirements and potentially enabling faster compression and decompression. The study also highlights the importance of choosing appropriate RNS moduli and operands to optimize performance. Overall, the evaluation suggests that RNS can be a viable and efficient alternative to binary arithmetic for implementing the Huffman algorithm, particularly in applications with high computational demands or limited hardware resources. However, further research is needed to explore the potential benefits and limitations of RNS in other areas of data compression and signal processing.

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“…Using arithmetic coding and the DCT, J. [3]. The suggested technique produced large compression ratios with good image reconstructed quality.…”

Section: Review Of Related Literaturementioning

confidence: 99%