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Python Imaging Library is a free and open-source additional library for the Python programming language that adds support for opening, manipulating, and saving many different image file formats. It is available for Windows, Mac OS X and Linux. The latest version of PIL is 1.1.7, was released in September 2009 and supports Python 1.5.2–2.7. [3]
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Examples of algorithms for this task include New Edge-Directed Interpolation (NEDI), [1] [2] Edge-Guided Image Interpolation (EGGI), [3] Iterative Curvature-Based Interpolation (ICBI), [citation needed] and Directional Cubic Convolution Interpolation (DCCI). [4] A study found that DCCI had the best scores in PSNR and SSIM on a series of test ...
The simplest form of quantization is to simply assign 3 bits to red, 3 bits to green and 2 bits to blue, as the human eye is less sensitive to blue light. This creates a so called 3-3-2 8-bit color image, arranged like on the following table: Bit 7 6 5 4 3 2 1 0 Data R R R G G G B B. This process is sub optimal.
Group 4 compression is available in many proprietary image file formats as well as standardized formats such as TIFF, CALS, CIT (Intergraph Raster Type 24) and the PDF document format. G4 offers a small improvement over G3-2D by removing the end-of-line (EOL) codes. G3 and G4 compression both treat an image as a series of horizontal black ...
Often, the motivation is to achieve consistency in dynamic range for a set of data, signals, or images to avoid mental distraction or fatigue. For example, a newspaper will strive to make all of the images in an issue share a similar range of grayscale. Normalization transforms an n-dimensional grayscale image : {} {,..
Netpbm (formerly Pbmplus) is an open-source package of graphics programs and a programming library. It is used mainly in the Unix world, where one can find it included in all major open-source operating system distributions, but also works on Microsoft Windows, macOS, and other operating systems.
Clustering-based methods, where the gray-level samples are clustered in two parts as background and foreground, [4] [5] Entropy -based methods result in algorithms that use the entropy of the foreground and background regions, the cross-entropy between the original and binarized image, etc., [ 6 ]