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No-Reference Video Quality Assessment Based on Benford's Law and Perceptual Features
| Content Provider | MDPI |
|---|---|
| Author | Varga, Domonkos |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 2768 |
| e-ISSN | 20799292 |
| DOI | 10.3390/electronics10222768 |
| Journal | Electronics |
| Issue Number | 22 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-11-12 |
| Access Restriction | Open |
| Subject Keyword | Electronics Information and Library Science No-reference Video Quality Assessment Benford's Law Feature Extraction |
| Content Type | Text |
| Resource Type | Article |
