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Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network
Weixia Zhang, Kede Ma, Jia Yan +2
We propose a deep bilinear model for blind image quality assessment (BIQA) that handles both synthetic and authentic distortions. Our model consists of two convolutional neural net…
Intrinsic Image Popularity Assessment
Keyan Ding, Kede Ma, Shiqi Wang
The goal of research in automatic image popularity assessment (IPA) is to develop computational models that can accurately predict the potential of a social image to go viral on th…
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…
Efficient and Effective Context-Based Convolutional Entropy Modeling for Image Compression
Mu Li, Kede Ma, Jane You +2
Precise estimation of the probabilistic structure of natural images plays an essential role in image compression. Despite the recent remarkable success of end-to-end optimized imag…
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…