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Image Data Formats and Image Compression
Published in Elizabeth Berry, A Practical Approach to Medical Image Processing, 2007
Image data formats are an essential part of image processing activity because they define how the image data are stored and which additional information is saved together with the image data. Image compression is often a component feature of an image file format because it can reduce the size of the file for storage and transmission. An understanding of different ways of achieving compression is valuable to avoid the possibility of inadvertently changing pixel values through an inappropriate choice of method.
I
Published in Phillip A. Laplante, Dictionary of Computer Science, Engineering, and Technology, 2017
image file format a representation (usually binary) used by a computer system as an agreed format to store an image. Examples of image file formats include the Graphics Interchange Format (GIF) and Tagged Image File Format (TIFF).
Image Compression
Published in Vipin Tyagi, Understanding Digital Image Processing, 2018
The images are stored in a system using different image formats. An image file format is a standardized way of organizing and storing digital images. An image file format may store data in uncompressed or compressed form. Some of the common image formats are:Joint Photographic Experts Group (JPEG): JPEG is a very common compressed image file format that can store 24-bit photographic images, i.e., an image having upto 16 million colors, such as those used for imaging and multimedia applications. JPEG compressed images are usually stored in the JPEG File Interchange Format (JFIF) due to the difficulty of programming encoders and decoders that fully implement all aspects of the standard. Apart from JFIF, Exchangeable image file format (Exif) and ICC color profiles have also been proposed in recent years to address certain issues related to JPEG standard. JPEG standard was designed to compress, color or grayscale continuous-tone images.Graphics Interchange Format (GIF): GIF is a bitmap lossless image file format developed by Steve Wilhite in 1987 and commonly used in the World Wide Web due to its portability. GIF format is useful for black and white, grayscale images and color images having less than 256 colors. This format also supports animations and allows a separate palette of upto 256 colors for each frame. This format is well suited to simpler images such as graphics or logos with solid areas of color but, due to palette limitations, not very suitable for color photographs and other images with color gradients. Most color images are 24 bits per pixel and hence can’t be stored as GIF. To store such images in GIF format, the image must first be converted into an 8-bit image. Although GIF format is a lossless format for color images with less than 256 colors, a rich truecolor image may “lose” 99.998% of the colors.Portable Network Graphics (PNG): PNG image compression format was designed as a replacement for Graphics Interchange Format (GIF), as a measure to avoid infringement of patent on the LZW compression technique. This format is lossless and supports 8-bit palette images (with optional transparency for all palette colors) and 24-bit true-color (16 million colors) or 48-bit true-color with and without alpha channel. In general, an image in a PNG file format can be 10% to 30% more compressed in comparison to a GIF format. This format maintains a trade-off between file size and image quality when the image is compressed.
A time series decomposition approach to detect coal fires in parts of the Gondwana coalfields of India from VIIRS data
Published in Journal of Spatial Science, 2023
Ritesh Mujawdiya, R. S. Chatterjee, Dheeraj Kumar
The VNP21A2 data files are provided in Hierarchical Data Format version 5 (HDF5). A single file contains 11 bands. These bands include emissivity maps in wavelength ranges 8.4–8.7 µm, 10.26–11.26 µm, and 11.54–12.49 µm, daytime and nighttime LST maps, as well as quality control, view angle, and view time bands for LST maps. The required bands were extracted from HDF files and converted into Tag Image File Format (TIFF). The LST maps have sinusoidal projection; therefore, their projection was changed to the WGS84 UTM zone 45 projected coordinate system. The data is available in rescaled digital numbers that must be multiplied by 0.02 to obtain actual LST values. The LAADS web interface provides VIIRS images in tiles. Due to the large size of the study area, a total of three tile locations were covered by the study area. Each tile location had 322 daytime and 322 nighttime LST maps. Therefore, the corresponding tiles were mosaicked to form full images covering the entire study area.
Simplified Prediction Method for Detecting the Emergency Braking Intention Using EEG and a CNN Trained with a 2D Matrices Tensor Arrangement
Published in International Journal of Human–Computer Interaction, 2023
Hermes J. Mora, Esteban J. Pino
The TensorFlow (TF) algorithm is a Python-open source library for numerical calculus making machine learning faster and easier (Python, 2019). The usage of TF to design and train CNNs is reasonably easy due to the strong support in Artificial Intelligence that has a vast number of functions to manage the input data. We can build a data generator with the (.numpy) extension that corresponds to a Python library implemented for working with N-dimensional arrays. With this in mind, the input data can be used directly as a large array configured by matrices. It is no necessary a dataset as RGB or grayscale images. Implementing a tensor (n-dimensional matrix) implies that the network designer reduces the processing time and computer resources when training the network. Based on Table 2, the dataset for each of the six electrode groups is configured directly as a (.numpy) array, Figure 3(b). By contrast, the image groups used to train our CNN through grayscale images and compare the CNN results are converted into the common image file format (.png). There are six electrode groups (4, 8, 13, 18, 33, 59) that result in six different 2 D-tensors. The height of each 2 D matrix varies according to the number of electrodes. The width of the matrix (400 samples) because there is no variation in the length of segments. The number of images in each image-set is significantly reduced given the number of electrodes used in each group.
Assessment of reservoir sedimentation of irrigation dams in northern Ghana
Published in Lake and Reservoir Management, 2020
Thomas A. Adongo, Nicholas Kyei-Baffour, Felix K. Abagale, Wilson A. Agyare
Multitemporal and multisensor satellite imageries of the reservoir catchments were acquired for the purpose of determining the land-use/land-cover (LULC) classes. Landsat 8 OLI images of scene 195/52 of the year 2016 were used for the study. Two software packages, ERDAS Imagine version 10.4 and ArcGIS version 10.4, were used to process the satellite images for layer stacking, mosaicking, georeferencing, subsetting, and training of the images according the area of interest (AOI). Using ERDAS, the raw satellite images were converted from tag image file format (tiff) to IMG format in order to be compatible with other ERDAS Imagine files. The UTM Zone 30 N Coordinate on the WGS84 was used to geocode the imported images. The Landsat 8 OLI images were georeferenced using ground control points collected from a shapefile created from samples recorded from the field with root mean square error (RMSE) of 0.015. The georeferenced images of the reservoir catchments were used for an image-to-image registration of the other images using the same AOI. The area of each catchment was used in the clipping and subsetting of the images to ensure faster processing.