access icon free No-reference video quality assessment method based on spatio-temporal features using the ELM algorithm

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.

Inspec keywords: data compression; learning (artificial intelligence); video coding; feature extraction; feedforward neural nets

Other keywords: full-reference metrics; single-hidden layer feedforward neural network; extreme learning machine algorithm; ELM algorithm; no-reference video quality assessment; Pearson correlation coefficient; spatio-temporal features; reference video quality assessment method; Spearman correlation coefficient; quality monitoring; joint photographic experts group no-reference; LIVE video data base; video transmission; stop criteria; reception system

Subjects: Image and video coding; Neural computing techniques; Video signal processing; Computer vision and image processing techniques; Knowledge engineering techniques

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