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Image Enhancement
Published in Christopher M. Hayre, William A. S. Cox, General Radiography, 2020
The purpose of image compression is to reduce file size in respect to storage and to ease image transfer across networks. Historically, transfer and interpretation of image data between different systems was problematic as there was no commonly agreed set of rules to define the format of the data files. To overcome this problem, the Digital Imaging and Communications in Medicine (DICOM) standard was developed which defines a set of instructions when transferring image data between two different systems. DICOM groups information into data sets consisting of a header containing demographic information and the image data (Varma, 2012). Effectively DICOM can be likened to a language interpreter working with a patient who does not speak English. DICOM does not convert image data but structures the data appropriately so another system recognizes fields such as name, gender, etc. To use the analogy of a language interpreter, their role would be to ensure any communication was converted to the appropriate language but was also grammatically correct. It is the accuracy of the grammar which mirrors what DICOM effectively does as grammar is a set of structural rules within any language. Therefore, DICOM determines the structure of image data and where specific image data should be within the file (Oakley, 2003). As a result, all modalities now have DICOM compatibility to a certain extent although the level of compatibility may vary.
Adaptive Fractal Image Coding Using Differential Scheme for Compressing Medical Images
Published in J. Dinesh Peter, Steven Lawrence Fernandes, Carlos Eduardo Thomaz, Advances in Computerized Analysis in Clinical and Medical Imaging, 2019
P. Chitra, M. Mary Shanthi Rani, V. Sivakumar
Digital image occupies voluminous data storage for storing and transmitting data. Image compression plays a vital role for reducing the storage space by eliminating redundant and irrelevant storage space [1-3]. It also represents an image with a reduced bit storage. Image compression is widely classified into lossy and lossless compression techniques. Lossless compression technique perfectly reconstructs the image without losing any information. Most of the research problems prefer lossless technique for the reason of image quality. Despite the importance of image quality in medical field, compression is essential for transmitting voluminous image data [4-6]. So, lossless image compression is the preferred method for medical image compression as high quality is required for accurate diagnosis. In lossy image compression process, the image will be represented by eliminating the redundant and irrelevant information from the original data. Generally, lossy image compression is a best choice for photographic/still images, as the human vision could not identify the minor changes in the image visually. Fractal coding is adopted for lossy compression process [7-9].
Image Compression
Published in Vipin Tyagi, Understanding Digital Image Processing, 2018
Generally, the main aim of any image compression technique is to reduce the quantity of data required to represent an image, without compromising the quality of the original image. However, minor loss in image quality is sometimes acceptable in certain applications, at the cost of amount of data reduced which is required to represent an image (Fig. 10.2). Based on different requirements, the image compression techniques can be broadly classified into two different classes: Lossless Compression TechniquesLossy Compression Techniques
Adaptive deblocking technique based on separate modes for removing compression effects in JPEG coded images
Published in International Journal of Computers and Applications, 2021
Amanpreet Kaur, Jagroop Singh Sidhu, Jaskarn Singh Bhullar
Image compression plays a vital role in several applications such as multimedia processing, digital library, teleconferencing, digital photography, mobile communications, interactive TV, medical imagining, remote sensing etc. The primary objective of image compression is to reduce the transmission cost and storage space and also maintain the perceptual quality of an image without any loss of tangible information. The transmission of huge data needs more time to transfer over the communication channel. Consequently, time and space are two fundamentals of computing components. In other words, image compression is one of the most popular and successful technologies, which compress the size of data according to display a given quality of information. Block-based Discrete Cosine Transform (BDCT) is mostly used to compress images and videos due to its excellent energy compaction property for highly correlated data. Therefore, BDCT makes a popular transform approach for image coding [1]. In BDCT, an image is divided into a number of blocks with size N × N, where N × N (2 × 2, 4 × 4, 8 × 8, 16 × 16, 32 × 32) is the size of sub-image block. Each block is predicted, quantized, and transformed independently which may result in correlation and continuity loss among surrounding blocks especially at low bit rate. However, blocking artifacts (tiling) are visible due to non-continuous pixel values [2–3].
An image compression model via adaptive vector quantization: hybrid optimization algorithm
Published in The Imaging Science Journal, 2020
Pratibha Pramod Chavan, B. Sheela Rani, M. Murugan, Pramod Chavan
Image compression is a method of removing or decreasing duplication in image representation in order to save communication and storage. As per the reconstructed image quality, the compression methods are usually classified as lossless or lossy [1,2]. Lossless compression guarantees that the rebuilt image will be a good replacement for the original. The compression ratio of this sort of compression is usually lower than that lossy compression. Lossless compression techniques include RLE, LZW, and entropy coding [3,4]. To achieve a higher compression ratio or a larger decrement data, some nonredundant information is discarded in the lossy compression. As a consequence, the reconstructed image is distorted at cost . Lossy compression is represented by JPEG, SPIHT, and fractal compression [5,6].
Binary medical image compression using the volumetric run-length approach
Published in The Imaging Science Journal, 2019
Erdoğan Aldemir, Gulay Tohumoglu, M. Alper Selver
Image compression is categorized under two main headings as lossy and lossless methods. While the data represented by the lossless techniques are perfectly reconstructed, the lossy methods cause information-loss that are regarded as the redundant part. Due to the fact that any loss of information in medical images that may induce false diagnosis causes catastrophic effects, the lossy methods are given less preference in the field of medical data compression even if they achieve higher compression ratios than lossless techniques [1].