Image compression techniques: A survey in lossless and lossy algorithms

Abstract

The bandwidth of the communication networks has been increased continuously as results of technological advances. However, the introduction of new services and the expansion of the existing ones have resulted in even higher demand for the bandwidth. This explains the many efforts currently being invested in the area of data compression. The primary goal of these works is to develop techniques of coding information sources such as speech, image and video to reduce the number of bits required to represent a source without significantly degrading its quality. With the large increase in the generation of digital image data, there has been a correspondingly large increase in research activity in the field of image compression. The goal is to represent an image in the fewest number of bits without losing the essential information content within. Images carry three main type of information: redundant, irrelevant, and useful. Redundant information is the deterministic part of the information, which can be reproduced without loss from other information contained in the image. Irrelevant information is the part of information that has enormous details, which are beyond the limit of perceptual significance (i.e., psychovisual redundancy). Useful information, on the other hand, is the part of information, which is neither redundant nor irrelevant. Human usually observes decompressed images. Therefore, their fidelities are subject to the capabilities and limitations of the Human Visual System.

Towards the Concurrent Execution of Multiple Hyperspectral Imaging Applications by Means of Computationally Simple Operations

“…On the contrary, the transmission bandwidth of the data link has been kept relatively stable [26,27]. Accordingly, for being able to transfer to the Earth surface all the acquired data, it is necessary to achieve much higher compression ratios, moving from lossless to lossy hyperspectral compression [28][29][30]. The alternative is to analyze on-board the hyperspectral data in order to transmit just the obtained results or discard the information that is not of interest for the targeted applications, which reduces the total amount of data to be sent to the Earth surface as well as the required compression ratios.…”

Analysis of Edge Detection on Compressed Images with Different Complexities

“…The size of the image can be large so that it is very impractical to store or transfer, especially when it comes to real-time image processing systems. For this reason, many image compression methods have been developed, but we can divide them all into lossy and lossless ones [1][2][3].…”

Pre and Postprocessing for JPEG to Handle Large Monochrome Images

“…In recent years, image compression has been considered as an attractive research field. Frequently, data are represented using large size images such as wallpaper and high quality media, which in turn need to be stored and transmitted without requiring large storage space or increased transmission rate of the communication channel [1]. In general, image compression with better quality reconstructed images is the main goal of any compression technique.…”

“…Image compression algorithms can be categorized into either lossless or lossy [1,3]. While lossless compression methods conserve the original image to be recovered completely after the decompression process [4], lossy compression uses the inherent redundancies found in an image, such as inter-pixel redundancy, psycho-visual redundancy, or coding redundancy, to decrease the data amount needed to represent the image [5][6][7].…”