Results 1 to 10 of about 7,591,877 (308)
MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment [PDF]
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022 No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual quality of images in accordance with human subjective perception. Unfortunately, existing NR-IQA methods are far from meeting the needs of predicting accurate quality scores on ...
Sidi Yang +6 more
semanticscholar +1 more source
CLIPScore: A Reference-free Evaluation Metric for Image Captioning [PDF]
Conference on Empirical Methods in Natural Language Processing, 2021 Image captioning has conventionally relied on reference-based automatic evaluations, where machine captions are compared against captions written by humans. This is in contrast to the reference-free manner in which humans assess caption quality.
Jack Hessel +4 more
semanticscholar +1 more source
Quadratic Fitting Model in No-Reference Image Quality Assessment [PDF]
Telfor Journal, 2023 The perceptual quality of image is affected by distortions during compression, delivery and storage. Distortions also impact automatic image quality assessment (IQA) that needs to be highly correlated with subjective scores.
A. Gavrovska +4 more
doaj +1 more source
No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System
Applied Sciences, 2022 Provisioning the stereoscopic 3D (S3D) video transmission services of admissible quality in a wireless environment is an immense challenge for video service providers.
Md Mehedi Hasan +4 more
doaj +1 more source
To Refer or Not to Refer in Teledermoscopy: Retrospective Study
JMIR Dermatology, 2022 Background Challenges remain for general practitioners (GPs) in diagnosing (pre)malignant and benign skin lesions. Teledermoscopy (TDsc) supports GPs in diagnosing these skin lesions guided by teledermatologists' (TDs) diagnosis and advice and prevents unnecessary referrals to dermatology care.
Esmée Tensen +4 more
openaire +3 more sources
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment [PDF]
IEEE Transactions on Image Processing, 2016 We present a deep neural network-based approach to image quality assessment (IQA). The network is trained end-to-end and comprises ten convolutional layers and five pooling layers for feature extraction, and two fully connected layers for regression ...
S. Bosse +4 more
semanticscholar +1 more source
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement [PDF]
Computer Vision and Pattern Recognition, 2020 The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network.
Chunle Guo +6 more
semanticscholar +1 more source
Benchmarking Underwater Image Enhancement and Restoration, and Beyond
IEEE Access, 2020 Image enhancement and restoration is among the most investigated topics in the field of underwater machine vision. The objective image quality assessment is a fundamental part of optimizing underwater enhancement and restoration technologies.
Guojia Hou +5 more
doaj +1 more source
SEM Image Quality Assessment Based on Intuitive Morphology and Deep Semantic Features
IEEE Access, 2022 The widespread use of scanning electron microscopy (SEM) has increased the requirements for SEM image quality. SEM images obtained by electron beam feedback have more complex texture features than natural images obtained by optical imaging, and this ...
Haoran Wang +5 more
doaj +1 more source
MRET: Multi-resolution transformer for video quality assessment
Frontiers in Signal Processing, 2023 No-reference video quality assessment (NR-VQA) for user generated content (UGC) is crucial for understanding and improving visual experience. Unlike video recognition tasks, VQA tasks are sensitive to changes in input resolution.
Junjie Ke +4 more
doaj +1 more source