Quality index evaluation of videos based on fuzzy interface system

Objective image quality assessment (IQA) is a challenge facing digital image and video processing systems because image quality is distorted during various applications, including restoration, compression, storage and transmission. Therefore, this study proposes a new methodology based on a fuzzy interface system called quality evaluation system (QES) to measure the total quality index (TQI) of input videos with many distorted situations. Nine quality metrics [peak signal-to-noise ratio, visual signal-to-noise ratio, weighted signal-to-noise ratio, structural similarity (SSIM), multi-scale SSIM, universal image quality index, visual information fidelity, information fidelity criterion and noise quality measure] were used as inputs for three fuzzy logic controller systems, and their outputs were set as inputs to another fuzzy logic controller system to obtain the TQI of the input video. This process contributes to obtain clear performance of the quality index of the input video despite the failure of some IQA methods in providing quality performance of the input video in some situations. The proposed QES is tested on four videos captured with different digital cameras under different noise levels. Furthermore, the authors evaluated the proposed QES on three databases (TID2008, TID2013 and LIVE) to improve the experimental results. In addition, the authors used wavelet decomposition and image de-noising to enhance the standard Eulerian video magnification technique. The proposed QES was also used to prove that the authors' magnification system has better magnification quality index than other magnification techniques.