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Modular Blind Video Quality Assessment
March 1, 2024, 5:47 a.m. | Wen Wen, Mu Li, Yabin Zhang, Yiting Liao, Junlin Li, Li Zhang, Kede Ma
cs.CV updates on arXiv.org arxiv.org
Abstract: Blind video quality assessment (BVQA) plays a pivotal role in evaluating and improving the viewing experience of end-users across a wide range of video-based platforms and services. Contemporary deep learning-based models primarily analyze the video content in its aggressively downsampled format, while being blind to the impact of actual spatial resolution and frame rate on video quality. In this paper, we propose a modular BVQA model, and a method of training it to improve its …
abstract analyze arxiv assessment blind cs.cv deep learning eess.iv experience format impact modular pivotal platforms quality role services spatial type video video quality
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