Deep Learning of Human Visual Sensitivity in Image Quality Assessment Framework

260 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric (2018, methodology): “Kim and Lee [25] use a CNN to predict visual similarity by training on low-level differences.”
No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency (2021, methodology): “Early version of deep learning NR-IQA methods [31, 45, 46, 34, 46, 35] leveraged deep features from CNNs [29, 30] while pre-trained on large classification dataset ImageNet [47].”
TOPIQ: A Top-Down Approach From Semantics to Distortions for Image Quality Assessment (2023, background): “However, early end-to-end works [20], [21] suffer from over-fitting.”
Deep Learning for Image Super-Resolution: A Survey (2019, background): “[68] propose DeepQA, which predicts visual similarity of images by training on triplets of distorted images, objective error maps, and subjective scores.”
PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference (2018, methodology): “, [17, 24,28]), in this paper we only compare against the three that performed the best on established datasets based on their published results [7, 25].”
Deep CNN-Based Blind Image Quality Predictor (2019, methodology): “We compared DIQA with four FR-IQA methods (PSNR, SSIM [35], FSIMc [36], and DeepQA [37]) and seven NR-IQA”
State of the Art in Defect Detection Based on Machine Vision (2021, background): “Images play a very important role in human perception [15].”