all AI news
No-Reference Light Field Image Quality Assessment Based on Spatial-Angular Measurement. (arXiv:1908.06280v2 [eess.IV] UPDATED)
Feb. 21, 2022, 2:10 a.m. | Likun Shi, Wei Zhou, Zhibo Chen, Jinglin Zhang
cs.CV updates on arXiv.org arxiv.org
Light field image quality assessment (LFI-QA) is a significant and
challenging research problem. It helps to better guide light field acquisition,
processing and applications. However, only a few objective models have been
proposed and none of them completely consider intrinsic factors affecting the
LFI quality. In this paper, we propose a No-Reference Light Field image Quality
Assessment (NR-LFQA) scheme, where the main idea is to quantify the LFI quality
degradation through evaluating the spatial quality and angular consistency. We
first …
More from arxiv.org / cs.CV updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Stagista Technical Data Engineer
@ Hager Group | BRESCIA, IT
Data Analytics - SAS, SQL - Associate
@ JPMorgan Chase & Co. | Mumbai, Maharashtra, India