K
Kede Ma
Researcher at City University of Hong Kong
Publications - 90
Citations - 6408
Kede Ma is an academic researcher from City University of Hong Kong. The author has contributed to research in topics: Image quality & Computer science. The author has an hindex of 27, co-authored 73 publications receiving 3768 citations. Previous affiliations of Kede Ma include Courant Institute of Mathematical Sciences & Hong Kong Polytechnic University.
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Reversible Data Hiding in Encrypted Images by Reserving Room Before Encryption
TL;DR: This paper proposes a novel method by reserving room before encryption with a traditional RDH algorithm, and thus it is easy for the data hider to reversibly embed data in the encrypted image.
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Perceptual Quality Assessment for Multi-Exposure Image Fusion
TL;DR: This paper proposes a novel objective image quality assessment (IQA) algorithm for MEF images based on the principle of the structural similarity approach and a novel measure of patch structural consistency and shows that the proposed model well correlates with subjective judgments and significantly outperforms the existing IQA models for general image fusion.
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Waterloo Exploration Database: New Challenges for Image Quality Assessment Models
TL;DR: This work establishes a large-scale database named the Waterloo Exploration Database, which in its current state contains 4744 pristine natural images and 94 880 distorted images created from them, and presents three alternative test criteria to evaluate the performance of IQA models, namely, the pristine/distorted image discriminability test, the listwise ranking consistency test, and the pairwise preference consistency test.
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End-to-End Blind Image Quality Assessment Using Deep Neural Networks
TL;DR: This work demonstrates the strong competitiveness of MEON against state-of-the-art BIQA models using the group maximum differentiation competition methodology and empirically demonstrates that GDN is effective at reducing model parameters/layers while achieving similar quality prediction performance.
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Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network
TL;DR: A deep bilinear model for blind image quality assessment that works for both synthetically and authentically distorted images and achieves state-of-the-art performance on both synthetic and authentic IQA databases is proposed.