NR-IQA
27 papers with code • 1 benchmarks • 1 datasets
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Most implemented papers
No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion
Recently, a huge amount of effort has been devoted to exploiting convolutional neural networks and other deep learning techniques for no-reference image quality assessment.
Content-Variant Reference Image Quality Assessment via Knowledge Distillation
The comparisons of distribution differences between HQ and LQ images can help our model better assess the image quality.
UID2021: An Underwater Image Dataset for Evaluation of No-reference Quality Assessment Metrics
Achieving subjective and objective quality assessment of underwater images is of high significance in underwater visual perception and image/video processing.
No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features
Specifically, we apply a broad spectrum of local and global feature vectors to characterize the variety of authentic distortions.
A Human Visual System Inspired No-Reference Image Quality Assessment Method Based on Local Feature Descriptors
Objective quality assessment of natural images plays a key role in many fields related to imaging and sensor technology.
Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment
The underlying mechanism of the proposed approach is based upon the mild assumption that the SCIs, which are not physically acquired, still obey certain statistics that could be understood in a learning fashion.
Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop
zwx8981/PerceptualAttack_BIQA
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No-reference image quality assessment (NR-IQA) aims to quantify how humans perceive visual distortions of digital images without access to their undistorted references.
Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images
Computer graphics images (CGIs) are artificially generated by means of computer programs and are widely perceived under various scenarios, such as games, streaming media, etc.
Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bank
huang-shirui/semi-uir
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CVPR 2023
Despite the remarkable achievement of recent underwater image restoration techniques, the lack of labeled data has become a major hurdle for further progress.
Blind Image Quality Assessment via Transformer Predicted Error Map and Perceptual Quality Token
srache/tempqt
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Specifically, we firstly generate the predicted error map by pre-training one model consisting of a Transformer encoder and decoder, in which the objective difference between the distorted and the reference images is used as supervision.
