Encoding using Greedy Algorithm and minHeap Project Description: Data Compression using Huffman Coding
The project aims to implement the renowned Huffman coding algorithm utilizing the custom PriorityQueue class, adhering to the principles of Object-Oriented Programming. Huffman coding is a highly effective method for data compression that assigns variable-length codes to characters based on their frequencies. To achieve this, two classes, namely HuffmanTree and HuffmanAlgorithm, will be developed to facilitate the implementation of Huffman codes.
The PriorityQueue class serves as a crucial component, providing a template-based implementation to prioritize elements based on their weights. This class will be utilized to store instances of the HuffmanTree class, ensuring that trees with lower weights are given higher priority. The PriorityQueue class employs the concept of a min Heap tree, allowing the prioritization of characters with minimal frequencies to construct the Huffman Tree.
The HuffmanTree class assumes the responsibility of creating and managing the Huffman tree. It encompasses the construction of various constructors, a copy constructor, a destructor, an assignment operator, and a method to traverse the tree and generate codes for individual letters. The ordering of trees within the PriorityQueue will rely on the cumulative sum of weights contained in each tree.
Leveraging the PriorityQueue, the HuffmanAlgorithm class implements the Huffman coding algorithm. It accepts the frequencies of each letter as input and constructs the Huffman tree accordingly. Furthermore, it computes the code for each character by traversing the tree. The class offers methods to encode a given word and produce the letter-to-code translation table.
Future Applications:
- Data Compression: Huffman coding plays a significant role in data compression algorithms, including popular file compression formats like ZIP and GZIP. Developing an understanding of Huffman coding and its implementation can pave the way for crafting efficient compression algorithms.
- Network Communication: Huffman coding finds applications in network communication protocols to reduce the size of data transmitted over networks. This optimization helps enhance bandwidth utilization and overall data transmission efficiency.
- Image and Video Compression: Huffman coding is extensively employed in image and video compression techniques, such as JPEG and MPEG. By applying Huffman coding to pixel or color values, the size of image and video files can be significantly reduced without substantial quality loss.
- Text Encoding: Huffman coding can be applied in text encoding schemes like ASCII and Unicode. It enables the efficient representation of characters based on their frequency of occurrence, optimizing the storage and transmission of text data.