Quality index evaluation of videos based on fuzzy interface system. Issue 5 (1st May 2017)

• Record Type: Journal Article
• Authors: Al‐Naji, Ali; Lee, Sang‐Heon; Chahl, Javaan
• Is Part Of: IET image processing. Volume 11:Issue 5(2017)
• Page Start: 292
• Page End: 300
• Publication Date: 2017-05-01
• Subjects: video signal processing -- fuzzy control -- Gaussian noise -- image denoising -- wavelet transforms -- image enhancement; TID2008 databases -- TID2013 databases -- LIVE databases -- wavelet decomposition -- image denoising -- standard Eulerian video magnification technique -- image blurring -- Poisson noise -- Gaussian noise -- quality performance -- fuzzy logic controller systems -- noise quality measure -- information fidelity criterion -- visual information fidelity -- universal image quality index -- multiscale SSIM -- structural similarity -- weighted signal‐to‐noise ratio -- visual signal‐to‐noise ratio -- peak signal‐to‐noise ratio -- TQI -- total quality index -- quality evaluation system -- image quality distortion -- video processing systems -- digital image processing systems -- objective IQA -- objective image quality assessment -- fuzzy interface system -- video quality index evaluation; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.0569
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.