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SSIM
The structural similarity index measure (SSIM) is a method for predicting the perceived quality of digital television and cinematic pictures, as well as other kinds of digital images and videos. SSIM is used for measuring the similarity between two images. The SSIM index is a full reference metric; in other words, the measurement or prediction of image quality is based on an initial uncompressed or distortion-free image as reference. SSIM is a perception-based model that considers image degradation as ''perceived change in structural information'', while also incorporating important perceptual phenomena, including both luminance masking and contrast masking terms. The difference with other techniques such as MSE or PSNR is that these approaches estimate ''absolute errors''. Structural information is the idea that the pixels have strong inter-dependencies especially when they are spatially close. These dependencies carry important information about the structure of the objects i ...
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Video Quality
Video quality is a characteristic of a video passed through a video transmission or processing system that describes perceived video degradation (typically, compared to the original video). Video processing systems may introduce some amount of distortion or artifacts in the video signal that negatively impacts the user's perception of a system. For many stakeholders in video production and distribution, assurance of video quality is an important task. Video quality evaluation is performed to describe the quality of a set of video sequences under study. Video quality can be evaluated objectively (by mathematical models) or subjectively (by asking users for their rating). Also, the quality of a system can be determined offline (i.e., in a laboratory setting for developing new codecs or services), or in-service (to monitor and ensure a certain level of quality). From analog to digital video Since the world's first video sequence was recorded and transmitted, many video processing sys ...
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Peak Signal-to-noise Ratio
Peak signal-to-noise ratio (PSNR) is an engineering term for the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Because many signals have a very wide dynamic range, PSNR is usually expressed as a logarithmic quantity using the decibel scale. PSNR is commonly used to quantify reconstruction quality for images and video subject to lossy compression. Definition PSNR is most easily defined via the mean squared error (''MSE''). Given a noise-free ''m''×''n'' monochrome image ''I'' and its noisy approximation ''K'', ''MSE'' is defined as : \mathit = \frac\sum_^\sum_^ (i,j) - K(i,j)2. The PSNR (in dB) is defined as : \begin \mathit &= 10 \cdot \log_ \left( \frac \right) \\ &= 20 \cdot \log_ \left( \frac \right) \\ &= 20 \cdot \log_(\mathit_I) - 10 \cdot \log_ (\mathit). \end Here, ''MAXI'' is the maximum possible pixel value of the image. When the pixels are represented using 8 bits per ...
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Alan Bovik
Alan Conrad Bovik (born June 25, 1958) is an American engineer, vision scientist, and educator. He is a professor at the University of Texas at Austin (UT-Austin), where he holds the Cockrell Family Regents Endowed Chair in the Cockrell School of Engineering and is Director of the Laboratory for Image and Video Engineering (LIVE). He is a faculty member in the UT-Austin Department of Electrical and Computer Engineering, the Machine Learning Laboratory, the Institute for Neuroscience, and the Wireless Networking and Communications Group. Bovik received a Primetime Emmy Award in 2015 for his development of perception-based video quality measurement tools that are now standards in television production. He also received a Technology and Engineering Emmy Award in 2021 for the “development of perceptual metrics for video encoding optimization.” Work Al Bovik was educated at the University of Illinois at Urbana–Champaign (PhD 1984). He has made numerous fundamental contributions ...
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Eero Simoncelli
Eero Simoncelli is an American computational neuroscientist and Silver Professor at New York University. He was a Howard Hughes Medical Institute Investigator from 2000 to 2020. In 2020, he became the inaugural director of the Center for Computational Neuroscience at the Flatiron Institute of the Simons Foundation. Education and early career Simoncelli graduated summa cum laude with a bachelor's degree in physics at Harvard University in 1984. He then attended Cambridge University on a Knox Fellowship to study the Mathematical Tripos, after which he joined the graduate program at the Massachusetts Institute of Technology in electrical engineering and computer science. He received his master's degree in 1988 and his PhD in 1993. He then joined the faculty at the University of Pennsylvania as an assistant professor, and in 1996 he moved to New York University. Awards and professional recognition In 2009, he became an IEEE Fellow. He received an Engineering Emmy Award in 2015 with ...
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Primetime Engineering Emmy Award
The Primetime Engineering Emmy Awards, or Engineering Emmys, are one of two sets of Emmy Awards that are presented for outstanding achievement in engineering development in the television industry. The Primetime Engineering Emmys are presented by the Academy of Television Arts & Sciences (ATAS), while the separate Technology and Engineering Emmy Awards are given by its sister organization, the National Academy of Television Arts and Sciences (NATAS). The Primetime Engineering Emmy is presented to an individual, company or organization for engineering developments so significant an improvement on existing methods or so innovative in nature that they materially affect the transmission, recording or reception of television. The award is determined by a jury of highly qualified, experienced engineers in the television industry. In addition, since 2003 the ATAS also bestows in most years the Philo T. Farnsworth Award, which is a Primetime Engineering Emmy Award given to honor companie ...
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Image Quality
Image quality can refer to the level of accuracy with which different imaging systems capture, process, store, compress, transmit and display the signals that form an image. Another definition refers to image quality as "the weighted combination of all of the visually significant attributes of an image". The difference between the two definitions is that one focuses on the characteristics of signal processing in different imaging systems and the latter on the perceptual assessments that make an image pleasant for human viewers. Image quality should not be mistaken with image fidelity. Image fidelity refers to the ability of a process to render a given copy in a perceptually similar way to the original (without distortion or information loss), i.e., through a digitization or conversion process from analog media to digital image. The process of determining the level of accuracy is called Image Quality Assessment (IQA). Image quality assessment is part of the quality of experience mea ...
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Dynamic Range
Dynamic range (abbreviated DR, DNR, or DYR) is the ratio between the largest and smallest values that a certain quantity can assume. It is often used in the context of signals, like sound and light. It is measured either as a ratio or as a base-10 ( decibel) or base-2 (doublings, bits or stops) logarithmic value of the difference between the smallest and largest signal values. Electronically reproduced audio and video is often processed to fit the original material with a wide dynamic range into a narrower recorded dynamic range that can more easily be stored and reproduced; this processing is called dynamic range compression. Human perception The human senses of sight and hearing have a relatively high dynamic range. However, a human cannot perform these feats of perception at both extremes of the scale at the same time. The human eye takes time to adjust to different light levels, and its dynamic range in a given scene is actually quite limited due to optical glare. The ins ...
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Image Restoration
Image restoration is the operation of taking a corrupt/noisy image and estimating the clean, original image. Corruption may come in many forms such as motion blur, noise and camera mis-focus. Image restoration is performed by reversing the process that blurred the image and such is performed by imaging a point source and use the point source image, which is called the Point Spread Function (PSF) to restore the image information lost to the blurring process. Image restoration is different from image enhancement in that the latter is designed to emphasize features of the image that make the image more pleasing to the observer, but not necessarily to produce realistic data from a scientific point of view. Image enhancement techniques (like contrast stretching or de-blurring by a nearest neighbor procedure) provided by imaging packages use no ''a priori'' model of the process that created the image. With image enhancement noise can effectively be removed by sacrificing some resolution, ...
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Image Compression
Image compression is a type of data compression applied to digital images, to reduce their cost for storage or transmission. Algorithms may take advantage of visual perception and the statistical properties of image data to provide superior results compared with generic data compression methods which are used for other digital data. Lossy and lossless image compression Image compression may be lossy or lossless. Lossless compression is preferred for archival purposes and often for medical imaging, technical drawings, clip art, or comics. Lossy compression methods, especially when used at low bit rates, introduce compression artifacts. Lossy methods are especially suitable for natural images such as photographs in applications where minor (sometimes imperceptible) loss of fidelity is acceptable to achieve a substantial reduction in bit rate. Lossy compression that produces negligible differences may be called visually lossless. Methods for lossy compression: * Transfor ...
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Gradient
In vector calculus, the gradient of a scalar-valued differentiable function of several variables is the vector field (or vector-valued function) \nabla f whose value at a point p is the "direction and rate of fastest increase". If the gradient of a function is non-zero at a point , the direction of the gradient is the direction in which the function increases most quickly from , and the magnitude of the gradient is the rate of increase in that direction, the greatest absolute directional derivative. Further, a point where the gradient is the zero vector is known as a stationary point. The gradient thus plays a fundamental role in optimization theory, where it is used to maximize a function by gradient ascent. In coordinate-free terms, the gradient of a function f(\bf) may be defined by: :df=\nabla f \cdot d\bf where ''df'' is the total infinitesimal change in ''f'' for an infinitesimal displacement d\bf, and is seen to be maximal when d\bf is in the direction of the gradi ...
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Radial Basis Function Interpolation
Radial basis function (RBF) interpolation is an advanced method in approximation theory for constructing high-order accurate interpolants of unstructured data, possibly in high-dimensional spaces. The interpolant takes the form of a weighted sum of radial basis functions, like for example Gaussian distributions. RBF interpolation is a mesh-free method, meaning the nodes (points in the domain) need not lie on a structured grid, and does not require the formation of a mesh. It is often spectrally accurate and stable for large numbers of nodes even in high dimensions. Many interpolation methods can be used as the theoretical foundation of algorithms for approximating linear operators, and RBF interpolation is no exception. RBF interpolation has been used to approximate differential operators, integral operators, and surface differential operators. Examples Let f(x) = \exp(x \cos(3 \pi x)) and let x_k = \frac, k=0, 1, \dots, 14 be 15 equally spaced points on the interval , 1/math>. ...
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YCbCr
YCbCr, Y′CbCr, or Y Pb/Cb Pr/Cr, also written as YCBCR or Y′CBCR, is a family of color spaces used as a part of the color image pipeline in video and digital photography systems. Y′ is the luma component and CB and CR are the blue-difference and red-difference chroma components. Y′ (with prime) is distinguished from Y, which is luminance, meaning that light intensity is nonlinearly encoded based on gamma corrected RGB primaries. Y′CbCr color spaces are defined by a mathematical coordinate transformation from an associated RGB primaries and white point. If the underlying RGB color space is absolute, the Y′CbCr color space is an absolute color space as well; conversely, if the RGB space is ill-defined, so is Y′CbCr. The transformation is defined iITU-T H.273 Nevertheless that rule does not apply to P3-D65 primaries used by Netflix with BT.2020-NCL matrix, so that means matrix was not derived from primaries, but now Netflix allows BT.2020 primaries (since 2021). ...
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