Signal-to-noise ratio is a measure used in science and engineering that compares the level of a desired signal to the level of background noise. SNR is defined as the ratio of signal power to the noise power, often expressed in decibels. A ratio higher than 1:1 indicates more signal than noise.
In statistics, the standard score is the signed fractional number of standard deviations by which the value of an observation or data point is above the mean value of what is being observed or measured. Observed values above the mean have positive standard scores, while values below the mean have negative standard scores.
Classical test theory (CTT) is a body of related psychometric theory that predicts outcomes of psychological testing such as the difficulty of items or the ability of test-takers. It is a theory of testing based on the idea that a person’s observed or obtained score on a test is the sum of a true score and an error score. Generally speaking, the aim of classical test theory is to understand and improve the reliability of psychological tests.
In statistics, multivariate analysis of variance (MANOVA) is a procedure for comparing multivariate sample means. As a multivariate procedure, it is used when there are two or more dependent variables, and is typically followed by significance tests involving individual dependent variables separately. It helps to answer:
- Do changes in the independent variable(s) have significant effects on the dependent variables?
- What are the relationships among the dependent variables?
- What are the relationships among the independent variables?
In statistics, the Neyman–Pearson lemma was introduced by Jerzy Neyman and Egon Pearson in a paper in 1933.
Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.
PSQM is a computational and modeling algorithm defined in ITU Recommendation ITU-T P.861 that objectively evaluates and quantifies voice quality of voice-band speech codecs. It may be used to rank the performance of these speech codecs with differing speech input levels, talkers, bit rates and transcodings. The ITU-T has Withdrawn P.861 and replaced it with P.862 (PESQ) which contains an improved speech assessment algorithm.
Subjective video quality is video quality as experienced by humans. It is concerned with how video is perceived by a viewer and designates their opinion on a particular video sequence and therefore related to the field of Quality of Experience. The measurement of subjective video quality is necessary since objective quality assessment algorithms such as PSNR have been shown to correlate badly with ratings. Subjective ratings may also be used as ground truth to develop new algorithms.
The structural similarity (SSIM) index is a method for predicting the perceived quality of digital television and cinematic pictures, as well as other kinds of digital images and videos. The early version of the model was developed in the Laboratory for Image and Video Engineering (LIVE) at The University of Texas at Austin and the full version was further developed jointly with the Laboratory for Computational Vision (LCV) at New York University. Further variants of the model were developed in the Image and Visual Computing Laboratory at University of Waterloo and have been commercially marketed.
Quality of Experience is a measure of the delight or annoyance of a customer's experiences with a service. QoE focuses on the entire service experience; it is a holistic concept, similar to the field of User Experience, but with its roots in telecommunication. QoE is an emerging multidisciplinary field based on social psychology, cognitive science, economics, and engineering science, focused on understanding overall human quality requirements.
In relativistic physics, the electromagnetic stress–energy tensor is the contribution to the stress–energy tensor due to the electromagnetic field. The stress–energy tensor describes the flow of energy and momentum in spacetime. The electromagnetic stress–energy tensor contains the negative of the classical Maxwell stress tensor that governs the electromagnetic interactions.
In quantum computing, quantum finite automata (QFA) or quantum state machines are a quantum analog of probabilistic automata or a Markov decision process. They are related to quantum computers in a similar fashion as finite automata are related to Turing machines. Several types of automata may be defined, including measure-once and measure-many automata. Quantum finite automata can also be understood as the quantization of subshifts of finite type, or as a quantization of Markov chains. QFAs are, in turn, special cases of geometric finite automata or topological finite automata.
Genista Corporation uses computational models of human visual and auditory systems to measure what viewers see and hear. Resulting perceptual metrics complement existing technologies by predicting experienced quality measured by a Mean Opinion Score (MOS) formerly assessed using subjective tests from actual viewers.
Image quality can refer to the level of accuracy in 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 focus 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.
The signal-to-noise ratio (SNR) is used in imaging as a physical measure of the sensitivity of a imaging system. Industry standards measure SNR in decibels (dB) of power and therefore apply the 10 log rule to the "pure" SNRratio. In turn, yielding the "sensitivity." Industry standards measure and define sensitivity in terms of the ISO film speed equivalent; SNR:32.04 dB = excellent image quality and SNR:20 dB = acceptable image quality.
Contrast-to-noise ratio (CNR) is a measure used to determine image quality. CNR is similar to the metric, signal-to-noise ratio (SNR), but subtracts off a term before taking the ratio. This is important when there is a significant bias in an image, such as from haze. As can be seen in the picture at right, the intensity is rather high even though the features of the image are washed out by the haze. Thus this image may have a high SNR metric, but will have a low CNR metric.
Visual Information Fidelity (VIF) is a full reference image quality assessment index based on natural scene statistics and the notion of image information extracted by the human visual system. It was developed by Hamid R Sheikh and Alan Bovik at the Laboratory for Image and Video Engineering (LIVE) at the University of Texas at Austin in 2006 and shown to correlate very well with human judgments of visual quality. It is deployed in the core of the Netflix VMAF video quality monitoring system, which controls the picture quality of all encoded videos streamed by Netflix. This accounts for about 35% of all U.S. bandwidth consumption and an increasing volume of videos streamed globally.
Video Multimethod Assessment Fusion (VMAF) is an objective full-reference video quality metric developed by Netflix in cooperation with the University of Southern California and the Laboratory for Image and Video Engineering (LIVE) at The University of Texas at Austin. It predicts subjective video quality based on a reference and distorted video sequence. The metric can be used to evaluate the quality of different video codecs, encoders, encoding settings, or transmission variants.
