948 citations · 3k across the 36 of their papers we have counts for
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cs.CV2019★ 9 cited
Learning to Blindly Assess Image Quality in the Laboratory and Wild
Weixia Zhang, Kede Ma, Guangtao Zhai +1
Computational models for blind image quality assessment (BIQA) are typically trained in well-controlled laboratory environments with limited generalizability to realistically disto…
cs.CV2019★ 325 cited
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs
Kede Ma, Wentao Liu, Tongliang Liu +2
Objective assessment of image quality is fundamentally important in many image processing tasks. In this work, we focus on learning blind image quality assessment (BIQA) models whi…