ISSN: 0374-8588 Volume 21 Issue 13, December 2019 _________________________________________________________________________ 1595 Lossless Image Compression: A Review Paper Prashant Kumar Faculty of Engineering, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India ABSTRACT: In industrial, academic, defence and medical fields, image compression is one of the main and important applications. Larger image files cannot be easily and securely processed or stored. Therefore, for real-world applications, compressing images while preserving the highest possible quality is very critical.For image compression, lossy compression is widely popular and used in commercial applications. In order to perform successful image-related work, the quality needs to be high in many circumstances despite having a comparatively low file size. In this analysis, lossless compression algorithms are therefore used to compare the lossless algorithms and to check which algorithm makes the compression maintain a decent compression ratio of consistency. In this paper, various lossless image compression techniques are discussed in order to secure and maintain the data privacy during the image transmission over the communication channels. KEYWORDS:Communication Channel,Correlation Coefficient,Image Transmission, Lossless Compression. INTRODUCTION Image compression is a technique that is commonly used during image storage and processing to minimise the size of the image. With growing image quality and scale, compression has become critical in everyday life. With the increased use of cloud storage, compression plays an essential role in the online storage of a large number of images.When the amount of data required to display an image is reduced, and the space to store it is reduced, and the efficiency of image transfer increases, image compression is efficient.Using two-dimensional Cartesian coordinates, an image is digitally represented as I (m, n). The indices m, n are used to represent the image rows and columns, and the coordinate (m, n) represents the pixel from the top left corner of the image at the position where the representation begins. In abstract spaces dependent on the application, images may also be denoted. You may also use the coordinates to represent a space of three or more dimensions[1].