Electronics 10(22), pp. 2768.
ISSN/ISBN: Not available at this time. DOI: 10.3390/electronics10222768
Abstract: No-reference video quality assessment (NR-VQA) has piqued the scientific community’s interest throughout the last few decades, owing to its importance in human-centered interfaces. The goal of NR-VQA is to predict the perceptual quality of digital videos without any information about their distortion-free counterparts. Over the past few decades, NR-VQA has become a very popular research topic due to the spread of multimedia content and video databases. For successful video quality evaluation, creating an effective video representation from the original video is a crucial step. In this paper, we propose a powerful feature vector for NR-VQA inspired by Benford’s law. Specifically, it is demonstrated that first-digit distributions extracted from different transform domains of the video volume data are quality-aware features and can be effectively mapped onto perceptual quality scores. Extensive experiments were carried out on two large, authentically distorted VQA benchmark databases.
Bibtex:
@article{,
AUTHOR = {Varga, Domonkos},
TITLE = {No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features},
JOURNAL = {Electronics},
VOLUME = {10},
YEAR = {2021},
NUMBER = {22},
ARTICLE-NUMBER = {2768},
URL = {https://www.mdpi.com/2079-9292/10/22/2768},
ISSN = {2079-9292},
DOI = {10.3390/electronics10222768}
}
Reference Type: Journal Article
Subject Area(s): Computer Science, Image Processing