No‐reference video quality assessment method based on spatio‐temporal features using the ELM algorithm. Issue 7 (29th April 2020)

• Record Type: Journal Article
• Authors: da Silva, Wyllian Bezerra; Mikowski, Alexandre; Casali, Rafael Machado
• Is Part Of: IET image processing. Volume 14:Issue 7(2020)
• Page Start: 1316
• Page End: 1326
• Publication Date: 2020-04-29
• Subjects: data compression -- feedforward neural nets -- feature extraction -- learning (artificial intelligence) -- video coding; Pearson correlation coefficient -- Spearman correlation coefficient -- full‐reference metrics -- quality monitoring -- video transmission -- reception system -- reference video quality assessment method -- spatio‐temporal features -- ELM algorithm -- extreme learning machine algorithm -- single‐hidden layer feedforward neural network -- no‐reference video quality assessment -- stop criteria -- joint photographic experts group no‐reference -- LIVE video data base; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0941
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : This work presents an application of the extreme learning machine (ELM) algorithm based on a single‐hidden layer feedforward neural network for no‐reference video quality assessment. The present research introduces an augmented version of ELM through simple stop criteria, which proved the effectiveness of the video quality assessment method. The authors present empirical studies using LIVE video data base show that the proposed method delivers accuracy (Pearson's correlation coefficient) and monotonicity (Spearman's correlation coefficient) with subjective scores against no‐reference, Joint Photographic Experts Group No‐Reference, metric and full‐reference metrics, for instance, peak signal‐to‐noise ratio, structural similarity (SSIM) and multi‐scale‐SSIM indexes, and the proposed method is suitable for quality monitoring of video transmission and reception system.