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The metric is based on initial work from the group of Professor C.-C. Jay Kuo at the University of Southern California. [1] [2] [3] Here, the applicability of fusion of different video quality metrics using support vector machines (SVM) has been investigated, leading to a "FVQA (Fusion-based Video Quality Assessment) Index" that has been shown to outperform existing image quality metrics on a ...
Product One-way Two-way MANOVA GLM Mixed model Post-hoc Latin squares; ADaMSoft: Yes Yes No No No No No Alteryx: Yes Yes Yes Yes Yes Analyse-it: Yes Yes No
This is a list of content-control software and services. The software is designed to control what content may or may not be viewed by a reader, especially when used to restrict material delivered over the Internet via the Web, e-mail, or other means.
The code could also be entered into a dedicated remote control device that would then control the VCR. The number is generated by an algorithm from the date, time and channel of the programme; as a result, it does not rely on an over-the-air channel to serve as a conduit to ensure the recording is properly timed.
Perceptual Evaluation of Video Quality (PEVQ) is an end-to-end (E2E) measurement algorithm to score the picture quality of a video presentation by means of a 5-point mean opinion score (MOS). It is, therefore, a video quality model. PEVQ was benchmarked by the Video Quality Experts Group (VQEG
Verification is intended to check that a product, service, or system meets a set of design specifications. [6] [7] In the development phase, verification procedures involve performing special tests to model or simulate a portion, or the entirety, of a product, service, or system, then performing a review or analysis of the modeling results.
The predecessor of SSIM was called Universal Quality Index (UQI), or Wang–Bovik index, which was developed by Zhou Wang and Alan Bovik in 2001. This evolved, through their collaboration with Hamid Sheikh and Eero Simoncelli, into the current version of SSIM, which was published in April 2004 in the IEEE Transactions on Image Processing. [1]
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. [1] 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