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The Weissman score is a performance metric for lossless compression applications. It was developed by Tsachy Weissman, a professor at Stanford University, and Vinith Misra, a graduate student, at the request of producers for HBO's television series Silicon Valley, a television show about a fictional tech start-up working on a data compression algorithm.
WebP's lossless compression, a newer algorithm unrelated to VP8, was designed by Google software engineer Jyrki Alakuijala. It uses advanced techniques such as dedicated entropy codes for different color channels, exploiting 2D locality of backward reference distances and a color cache of recently used colors.
In the case of decreasing the pixel number (scaling down), this usually results in a visible quality loss. From the standpoint of digital signal processing, the scaling of raster graphics is a two-dimensional example of sample-rate conversion, the conversion of a discrete signal from a sampling rate (in this case, the local sampling rate) to ...
The .ZIP file format was designed by Phil Katz of PKWARE and Gary Conway of Infinity Design Concepts. The format was created after Systems Enhancement Associates (SEA) filed a lawsuit against PKWARE claiming that the latter's archiving products, named PKARC, were derivatives of SEA's ARC archiving system. [3]
NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]
Nonlinear dimensionality reduction, also known as manifold learning, is any of various related techniques that aim to project high-dimensional data, potentially existing across non-linear manifolds which cannot be adequately captured by linear decomposition methods, onto lower-dimensional latent manifolds, with the goal of either visualizing ...
The process of feature selection aims to find a suitable subset of the input variables (features, or attributes) for the task at hand.The three strategies are: the filter strategy (e.g., information gain), the wrapper strategy (e.g., accuracy-guided search), and the embedded strategy (features are added or removed while building the model based on prediction errors).
On March 6, 2018, Kubernetes Project reached ninth place in the list of GitHub projects by the number of commits, and second place in authors and issues, after the Linux kernel. [ 26 ] Until version 1.18, Kubernetes followed an N-2 support policy, meaning that the three most recent minor versions receive security updates and bug fixes. [ 27 ]