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Blind Deep S3D Image Quality Evaluation via Local to Global Feature Aggregation.

, , , and . IEEE Trans. Image Processing, 26 (10): 4923-4936 (2017)

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Blind Deep S3D Image Quality Evaluation via Local to Global Feature Aggregation., , , and . IEEE Trans. Image Processing, 26 (10): 4923-4936 (2017)Visual entropy: A new framework for quantifying visual information based on human perception., , and . ICIP, page 3485-3489. IEEE, (2017)Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super Resolution., , and . CoRR, (2022)Visual Preference Prediction for Enhanced Images on Ultra-High-Definition Display., , , , and . ICIP, page 3548-3552. IEEE, (2018)Virtual Reality Sickness Predictor: Analysis of visual-vestibular conflict and VR contents., , , , and . QoMEX, page 1-6. IEEE, (2018)Visual attention analysis on stereoscopic images for subjective discomfort evaluation., , , and . ICME, page 1-6. IEEE Computer Society, (2016)Deep Video Quality Assessor: From Spatio-Temporal Visual Sensitivity to a Convolutional Neural Aggregation Network., , , , and . ECCV (1), volume 11205 of Lecture Notes in Computer Science, page 224-241. Springer, (2018)Visual Preference Assessment on Ultra-High-Definition Images., , , and . TBC, 62 (4): 757-769 (2016)Deep Visual Discomfort Predictor for Stereoscopic 3D Images., , , and . IEEE Trans. Image Processing, 27 (11): 5420-5432 (2018)Multiple Level Feature-Based Universal Blind Image Quality Assessment Model., , , , and . ICIP, page 291-295. IEEE, (2018)