test message

Author: ayonious

README.md

@@ -1,73 +1,27 @@
 # File Compression
 
-[![CI](https://github.com/ayonious/File-Compression/workflows/CI/badge.svg)](https://github.com/ayonious/File-Compression/actions)
+[![Build and Test](https://github.com/ayonious/File-Compression/actions/workflows/build.yml/badge.svg)](https://github.com/ayonious/File-Compression/actions/workflows/build.yml)
+[![CircleCI](https://dl.circleci.com/status-badge/img/gh/ayonious/File-Compression/tree/master.svg?style=svg)](https://dl.circleci.com/status-badge/redirect/gh/ayonious/File-Compression/tree/master)
 [![codecov](https://codecov.io/gh/ayonious/File-Compression/branch/master/graph/badge.svg)](https://codecov.io/gh/ayonious/File-Compression)
 [![GitHub stars](https://img.shields.io/github/stars/ayonious/File-Compression?style=social)](https://github.com/ayonious/File-Compression/stargazers)
 
-A Java-based file compression application implementing two classic compression algorithms:
+A File Compression software that helps zip/Unzip files using these 2 algorihtms:
 
-1. **Huffman Coding** - Frequency-based compression
-2. **LZW (Lempel-Ziv-Welch)** - Dictionary-based compression
+1. Huffmans Code
+2. Lempel-Ziv-Wells algorithm
 
-## Compression Algorithms
+# About Huffmans Code
 
-### Huffman Coding
+The Huffmans algo creates a 1-1 mapping for each byte of the input file 
+and replaces each byte with the mapped bit sequence. For this you need 
+to store a dictionary that describes each 1-1 mapping of input byte and
+binary sequence.(which needs extraspace)
 
-**How it works:**
-Huffman coding is a **lossless compression algorithm** that assigns variable-length binary codes to characters based on their frequency. Characters that appear more frequently get shorter codes, while rare characters get longer codes.
+# About Lempel-Ziv-Wells
 
-**Example:**
-```
-Input text: "aabbc"
-Frequency: a=2, b=2, c=1
-
-Huffman codes assigned:
-a → 0
-b → 10
-c → 11
-
-Compressed: "0 0 10 10 11" = "00101011" (8 bits)
-Original: 5 characters × 8 bits = 40 bits
-Compression ratio: 80% reduction
-```
-
-**Key characteristics:**
-- Creates a frequency table and binary tree during compression
-- Requires storing the frequency table in the compressed file (overhead)
-- Works best with files that have **uneven character distribution**
-- Typical use cases: Text files, source code, log files
-
-### LZW (Lempel-Ziv-Welch)
-
-**How it works:**
-LZW is a **dictionary-based compression algorithm** that builds a dictionary of sequences on-the-fly. Instead of replacing individual bytes, it replaces repeated sequences of bytes with dictionary codes.
-
-**Example:**
-```
-Input text: "ABABABA"
-
-Initial dictionary (ASCII):
-256: A
-257: B
-...
-
-Compression process:
-- Read 'A' → output 256, add "AB" to dictionary (512)
-- Read 'B' → output 257, add "BA" to dictionary (513)
-- Read "AB" (found in dict!) → output 512, add "ABA" to dictionary (514)
-- Read "ABA" (found in dict!) → output 514
-
-Compressed: [256, 257, 512, 514]
-Original: 7 characters × 8 bits = 56 bits
-Compressed: 4 codes × ~9 bits = 36 bits
-Compression ratio: 35.7% reduction
-```
-
-**Key characteristics:**
-- **No dictionary is stored** - both compressor and decompressor build the same dictionary
-- Replaces **sequences of bytes**, not individual bytes
-- Works best with files that have **repetitive patterns**
-- Typical use cases: Log files, structured data (JSON, XML), source code with repeated patterns
+Unlike Huffmans code LZW dont need an extra dictionary to be saved. Also
+LZW does not create a mapping to byte to bin sequence. It creates mapping
+of multiple byte to binary sequence.
 
 ## Installation