How LZ78 Compression Algorithm Works

How LZ78 Compression Algorithm Works

In this post, we are going to explore LZ78, a lossless data-compression algorithm created by Lempel and Ziv in 1978. As an example, the GIF format is based on LZ78.

In this post, we are going to explore LZ78, a lossless data-compression algorithm created by Lempel and Ziv in 1978. As an example, the GIF format is based on LZ78. LZ78 takes advantage of a dictionary-based data structure to compress our data. In this case, it makes use of a trie data structure, as it’s more efficient for this compression technique.

The motivation behind this approach was to get rid of the parameterization that was required to optimize LZ77’s performance. For instance, in LZ77 if our search buffer was too small, the resulting encoding would require more space, although the compression time would be lower. On the opposite side, if our search buffer was too big, the compression time would take longer, but the required space would be lower. Obviously, there is not a universal set of parameters that would perform optimally in every case: we need to optimize these paremeters depending on the pattern of our input data. All in all, one of the main motivations behind LZ78 was to create a universal compression algorithm that does not require any knowledge on the input.

Compression

Before getting into the details of the compression process, we should define the trie data structure that will help us store our dictionary of string patterns (also known as phrases):

It is a non-binary tree.The root node represents an empty string.Every node is marked with its dictionary index.Every edge contains the character that should be added to get the value of the child node.In order to get the value of a node, we just need to traverse from our target node to the root node, reading the resulting string from top to bottom.

With this data structure in mind, we may define the compression process:

We read the next character of the input string.We check whether the current node (starting at the root node) has any outbound edge that contains the character read in step 1.If so, we follow the outbound edge and set the current node as the found node. Then, we go back to step 1. Otherwise, we create an outbound edge with the character read in step 1, leading to a new node. The new node is then marked with an incremental index. We create an encoding tuple with the following structure (pn, c)i where pn represents the index of the parent node, c represents the new character read in step 1 and i represents the index of the new node. After appending the new tuple to the encoding stream, we set the current node as the root node and return to step 1.

data-science

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

50 Data Science Jobs That Opened Just Last Week

Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

Applications Of Data Science On 3D Imagery Data

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.

Data Science Course in Dallas

Become a data analysis expert using the R programming language in this [data science](https://360digitmg.com/usa/data-science-using-python-and-r-programming-in-dallas "data science") certification training in Dallas, TX. You will master data...

32 Data Sets to Uplift your Skills in Data Science | Data Sets

Need a data set to practice with? Data Science Dojo has created an archive of 32 data sets for you to use to practice and improve your skills as a data scientist.

Data Cleaning in R for Data Science

A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.