April 4, 2024, 4:45 a.m. | Wei Sun, Wen Wen, Xiongkuo Min, Long Lan, Guangtao Zhai, Kede Ma

cs.CV updates on arXiv.org arxiv.org

arXiv:2307.13981v2 Announce Type: replace
Abstract: Blind video quality assessment (BVQA) plays an indispensable role in monitoring and improving the end-users' viewing experience in various real-world video-enabled media applications. As an experimental field, the improvements of BVQA models have been measured primarily on a few human-rated VQA datasets. Thus, it is crucial to gain a better understanding of existing VQA datasets in order to properly evaluate the current progress in BVQA. Towards this goal, we conduct a first-of-its-kind computational analysis of …

abstract analysis applications arxiv assessment blind cs.cv cs.mm datasets design eess.iv experience experimental human improvements improving media monitoring quality role the end type via video video quality vqa world

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Data Engineer

@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania

Artificial Intelligence – Bioinformatic Expert

@ University of Texas Medical Branch | Galveston, TX

Lead Developer (AI)

@ Cere Network | San Francisco, US