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The main goal of many-objective video quality metrics is to automatically estimate the average user's (viewer's) opinion on the quality of a video processed by a system. Procedures for subjective video quality measurements are described in ITU-R recommendation BT.500 and ITU-T recommendation P.910. In such tests, video sequences are shown to a ...
The motion measurements are also made using the space-time filter bank using a perceptually relevant measurement of phase-based optical flow. These measurements on the reference and test videos are then differentially combined and divisively normalized to produce a prediction of temporal picture quality.
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 ...
The main idea of measuring subjective video quality is similar to the mean opinion score (MOS) evaluation for audio. To evaluate the subjective video quality of a video processing system, the following steps are typically taken: Choose original, unimpaired video sequences for testing; Choose settings of the system that should be evaluated
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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. It is also used for measuring the similarity between two images.
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
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