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Image Compression
Published in Vipin Tyagi, Understanding Digital Image Processing, 2018
In comparison to JPEG, PNG format is better when the image has large, uniformly colored areas. PNG format also supports partial transparency that is useful in a number of applications, such as fades and anti-aliasing for text. PNG is a good choice for web browsers and can be fully streamed with a progressive display option.Tagged Image File Format (TIFF): TIFF is a flexible image file format that supports both lossless or lossy image compression. TIFF specification given by Aldus Corporation in 1986 normally saves eight bits per color (red, green, blue) for 24-bit images, and sixteen bits per color for 48-bit images. For handling images and data within a single file, TIFF file format has the header tags (size, definition, image-data arrangement, applied image compression) defining the image’s geometry. Thus, the details of the image storage algorithm are included as part of the file. TIFF format is widely accepted as a photograph file standard in the printing business, scanning, fax machines, word processing, optical character recognition, image manipulation and page-layout applications. TIFF files are not suitable for display on web due to their large size and most web browsers do not support these files.Bitmap (BMP): BMP file format by Microsoft is an uncompressed, simple, widely accepted format.
Production
Published in Wanda Grimsgaard, Design and Strategy, 2023
Lossy/destructive file type: Some file types are designed to take up less space (file size) and/or are optimised for use on the web or on a screen medium. These are called destructive file types because they use a technique called lossy compression of the content to a minimum. It also means that shades deteriorate, disappear or get smoothed out. File types of this type are .jpg, .png. and .gif. Other file formats such as .tif can also be compressed, however, they use a different type of compression, ‘loss-less’, which means that the visible content quality is not compromised.
Image and Its Properties
Published in Ravishankar Chityala, Sridevi Pudipeddi, Image Processing and Acquisition using Python, 2020
Ravishankar Chityala, Sridevi Pudipeddi
TIFF stands for Tagged Image File Format. Its extension is .tif or .tiff. The latest version of the TIFF standards is 6.0. It was created in the 80’s for storing and encoding scanned documents. It was developed by Aldus Corporation, which was later acquired by Adobe Systems. Hence, the copyright for TIFF standards is held by Adobe Systems.
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.
Mobile Phone based ensemble classification of Deep Learned Feature for Medical Image Analysis
Published in IETE Technical Review, 2020
Tamarafinide V. Dittimi, Ching Y. Suen
Thus, this study proposes a pre-trained mobile based application built using Unity 3D for the front-end development. Unity 3D is a useful development tool with the ability to work on both iPhone and Android-based platform and possesses the capacity to improve functionality and resolve the unpredictable growth hassles of mobile phone applications [17,18]. In addition, it gives an app developer the opportunity to scale many of the hurdles faced as it supports several scripting languages like C# and JavaScript and is not platform independent [19]. Developing a solution in Unity 3D is quick as it has many powerful convenience tools and integrated asset store with many free or easy-to-purchase plugins for any system. Similarly, the backend employed MATLAB 2018a because of the extensive mathematical functionality of the package; it enables inexperienced users to work with the toolbox in addition to providing automated, and batch standardization of analyses and statistical tools for data representation [20]. Lastly, the system accepts BMP, JPEG, TIFF, and PNG image formats. We opted for a shallow network for the system due to two key issues; deeper architectures like Resnet Requires a large amount of data and as we only had a limited sample data, more in-depth approaches like RESNET was unlikely to outperform other approaches.
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.