Szczegóły publikacji
Opis bibliograficzny
Objective video quality assessment method for face recognition tasks / Mikołaj LESZCZUK, Lucjan JANOWSKI, Jakub NAWAŁA, Atanas Boev // Electronics [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2079-9292. — 2022 — vol. 11 iss. 8 art. no. 1167, s. 1-23. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 21-23, Abstr. — Publikacja dostępna online od: 2022-04-07
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 139840 |
|---|---|
| Data dodania do BaDAP | 2022-04-15 |
| Tekst źródłowy | URL |
| DOI | 10.3390/electronics11081167 |
| Rok publikacji | 2022 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Electronics |
Abstract
Nowadays, there are many metrics for overall Quality of Experience (QoE), both those with Full Reference (FR), such as Peak Signal-to-Noise Ratio (PSNR) or Structural Similarity (SSIM), and those with No Reference (NR), such as Video Quality Indicators (VQI), which are successfully used in video processing systems to evaluate videos whose quality is degraded by different processing scenarios. However, they are not suitable for video sequences used for recognition tasks (Target Recognition Videos, TRV). Therefore, correctly estimating the performance of the video processing pipeline in both manual and Computer Vision (CV) recognition tasks is still a major research challenge. There is a need for objective methods to evaluate video quality for recognition tasks. In response to this need, we show in this paper that it is possible to develop the new concept of an objective model for evaluating video quality for face recognition tasks. The model is trained, tested and validated on a representative set of image sequences. The set of degradation scenarios is based on the model of a digital camera and how the luminous flux reflected from the scene eventually becomes a digital image. The resulting degraded images are evaluated using a CV library for face recognition as well as VQI. The measured accuracy of a model, expressed as the value of the F-measure parameter, is 0.87.
