Topics in Intelligent Computing and Industry Design (ICID) 2(2) (2020) 40-44 Quick Response Code Access this article online Website: www.intelcomp-design.com DOI: 10.26480/etit.02.2020.40.44 Cite The Article: Neha Sharma, Usha Batra(2020).Evaluation Of Lossless Algorithms For Data Compression. Topics In Intelligent Computing And Industry Design, 2(2): 40-44. ISBN: 978-1-948012-17-1 Ethics and Information Technology (ETIT) DOI: http://doi.org/10.26480/etit.02.2020.40.44 EVALUATION OF LOSSLESS ALGORITHMS FOR DATA COMPRESSION Neha Sharma* a , Usha Batra b a Research Scholar, G D Goenka University, Gurugram, 122103, India b Assistant Dean, G D Goenka University, Gurugram, 122103, India *Corresponding Author Email: nehasharma0110@gmail.com This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ARTICLE DETAILS ABSTRACT Article History: Received 25 October 2020 Accepted 26 November 2020 Available online 03 December 2020 Nowadays the communication and exchange of information over the internet which includes sending e-mails, text messages via online apps e.g. messengers, has become the need of the hour. While transmitting the data, some of the critical aspects like size of message or file, need to deal with extreme precaution as these are very crucial. Furthermore, the transmitting time is directly proportional to the file size i.e. lesser file size always take less time. Compression techniques are used to decrease the size of the file, meanwhile not impacting the quality. This paper demonstrates that the use of two lossless compression techniques on images so that they become suitable for information security using the techniques like steganography, cryptography etc. Thus, the objective of the paper is to reduce the image size by using Huffman encoding and run-length encoding algorithms. The algorithms are implemented and the performances are analysed by evaluating the results on different parameters, such as compression ratio, compressed file size, and compression & decompression time. The paper is concluded with the analysis of the results obtained. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd. KEYWORDS Compressed File size, Compression Ratio, Compression Time, Decompression Time. 1. INTRODUCTION Data compression is a procedure undertaken to convert the depicted data from one form to another form post compression, which contains the same information but with the reduced size (Patil, Kulat, 2017). The main benefits of compressing the data are the transmission bandwidth and the reduced storage capacity. This is very useful because storing and transmitting the files requires huge resources. One of the major area of application for the data compression techniques is on digital images. There are a number applications for image processing, such as medical imaging, satellite imaging, etc where the image size is large and it requires a more storage capacity or high bandwidth for transmitting it in its original form over a communication channel. After compression, when the size of data is reduced, it gives us the leverage to send more data. Therefore reducing the size to the half is equal to doubling its storage capacity. After getting this extra storage space, we can store data hieratically at better and higher levels that also avoids extra loads on input/output devices of computer system. 2. LITERATURE REVIEW RLE compression algorithm has been used widely because of its simplicity and less complexity. The algorithm is easy to implement and some of the researchers has made many modifications in it such as laying stress on the way the pixels are scanned like either the pixels are to scanned in row fashion, or column order (Karthikeyan, 2014). Some other authors have focused on calculating the bit depth of the runs of repeated pixels and tried to used enhance entropy coding to enhance (Suarjaya, 2012). The authors (Albahadily and Tsviatkou, 2016) have modified run length encoding algorithm in order to achieve reduced encoded size and reduced encoding time. The authors (Canard et. al., 2017) have used the run length algorithm in order to make it fit within the constraints of FHE execution and then analyzing it to the achieve the optimized FHE execution efficiency. Hybrid DWT-DCT algorithm is applied (Rafea and Salman, 2018) in order to compress the medical image and an adaptive RLE algorithm is used to encode the runs of zero created by the hybrid algorithm in order to achieve better compression ratio results. The authors (Khassaweneh and Alshorman, 2020) have used Frei-chen bases technique for compressing the large image data and then used a modified RLE algorithm in order to enhance the compression factor with out adding any distortion so as to receive the high-quality decompressed image. The use of Huffman encoding algorithm also brings the high-quality compression results. The authors (Erdal and Erguzen, 2019) have used Huffman encoding and arithmetic encoding in order to provide a solution to long bit sequences. The Huffman encoding in general gives better compression results (Ajala et al, 2018). The authors have combined to Huffman encoding with LZW algorithm in order to achieve cheap, reliable and efficient system. 3. DATA COMPRESSION There are two types of compression categories: Lossy Compression and Lossless Compression. 3.1 Lossy Compression Lossy compression technique is one that ignores the less important data. The file compressed using lossy technique will not be exactly same as the original file. After decompression we get the closer approximation of the original file (Klein et. al., 2019). Lossy type compression technique shrinks the bits by finding and eliminating unnecessary information This paper was presented at International Conference on Contemporary Issues in Computing (ICCIC-2020) - Virtual IETE Sector V, Salt Lake, Kolkata From 25th-26th July 2020