Image Compression & Transmission through Digital Communication System

Abstract

Image Compression addresses the problem of reducing the amount of data required to represent the digital image. Compression is achieved by the removal of one or more of three basic data redundancies: (1) Coding redundancy, which is present when less than optimal (i.e. the smallest length) code words are used; (2) Interpixel redundancy, which results from correlations between the pixels of an image; &/or (3) psycho visual redundancy which is due to data that is ignored by the human visual system (i.e. visually nonessential information). Huffman codes contain the smallest possible number of code symbols (e.g., bits) per source symbol (e.g., grey level value) subject to the constraint that the source symbols are coded one at a time. So, Huffman coding & Shannon Coding , which remove coding redundancies , when combined with the technique of image compression using Discrete Cosine Transform (DCT) & Discrete Wavelet Transform (DWT) helps in compressing the image data to a very good extent.

For the efficient transmission of an image across a channel, source coding in the form of image compression at the transmitter side & the image recovery at the receiver side are the integral process involved in any digital communication system. Other processes like channel encoding, signal modulation at the transmitter side & their corresponding inverse processes at the receiver side along with the channel equalization help greatly in minimizing the bit error rate due to the effect of noise & bandwidth limitations (or the channel capacity) of the channel.