DeepAI AI Chat
Log In Sign Up

N-ary Huffman Encoding Using High-Degree Trees – A Performance Comparison

by   Ioannis S. Xezonakis, et al.

In this paper we implement an n-ary Huffman Encoding and Decoding application using different degrees of tree structures. Our goal is to compare the performance of the algorithm in terms of compression ratio, decompression speed and weighted path length when using higher degree trees, compared to the 2-ary Huffman Code. The Huffman tree degrees that we compare are 2-ary, 3-ary, 4-ary, 5-ary, 6-ary, 7-ary, 8-ary and 16-mal. We also present the impact that branch prediction has on the performance of the n-ary Huffman Decoding.


page 1

page 2

page 3

page 4


An O(n n) time Algorithm for computing the Path-length Distance between Trees

Tree comparison metrics have proven to be an invaluable aide in the reco...

An Experimental Comparison of Old and New Decision Tree Algorithms

This paper presents a detailed comparison of a recently proposed algorit...

Trees from Functions as Processes

Levy-Longo Trees and Bohm Trees are the best known tree structures on th...

Finite-length performance comparison of network codes using random vs Pascal matrices

In this letter, we evaluate the finite-length performance of network cod...

On Prediction Using Variable Order Markov Models

This paper is concerned with algorithms for prediction of discrete seque...

RLFC: Random Access Light Field Compression using Key Views and Bounded Integer Encoding

We present a new hierarchical compression scheme for encoding light fiel...

Order-Preserving Key Compression for In-Memory Search Trees

We present the High-speed Order-Preserving Encoder (HOPE) for in-memory ...