Image Quality Assessment - Groups

LIVE In the Wild Image Quality Challenge Database

The dataset used for blind image quality assessment

Dataset
JSON

AIGIQA-20K

AIGIQA-20K: A large database for AI-generated image quality assessment.

Dataset
JSON

Kodak dataset

The dataset used in the paper is not explicitly described, but it is mentioned that the authors tested their model on various signal reconstruction tasks: 1D sinusoidal...

Dataset
JSON

AIGC2023

The dataset used in the paper for image quality assessment, consisting of AI-generated images.

Dataset
JSON

KADID

The dataset used in the paper for blind image quality assessment, consisting of images with various distortions.

Dataset
JSON

LIVEC

The dataset used in the paper for blind image quality assessment, consisting of images with various distortions.

Dataset
JSON

MUSIQ and KonIQ

MUSIQ and KonIQ are datasets used for image quality and aesthetic assessment.

Dataset
JSON

NIMA, TReS, and DB-

NIMA, TReS, and DB- are datasets used for image quality and aesthetic assessment.

Dataset
JSON

KADID-10K

The dataset contains images with various distortions, including blur, noise, and compression.

Dataset
JSON

TID2013

The dataset used in the paper for image quality assessment, consisting of images with various distortions.

Dataset
JSON

PIPAL

The dataset contains images processed by image restoration and enhancement methods, including deep learning-based methods.

Dataset
JSON

MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Asse...

The proposed method consists of four components: feature extractor using ViT, transposed attention block, scale swin transformer block, and a dual branch structure for...

Dataset
JSON

NTIRE 2022 challenge on perceptual image quality assessment

NTIRE 2022 challenge on perceptual image quality assessment.

Dataset
JSON

Kodak

The proposed system is composed of three neural networks (NN): an auto-regressive module (ARM) fψ, an upsampler fυ and a synthesis fθ.

Dataset
JSON

CSIQ

The proposed model is designed to capture distortion-related patterns using a task transfer strategy simulating the from easy to hard human learning law.

Dataset
JSON

LIVE

The proposed model is designed to capture distortion-related patterns using a task transfer strategy simulating the from easy to hard human learning law.

Dataset
JSON

BID

The diversity of distortion and variability in image content are the main reasons why BIQA is full of challenges.

Dataset
JSON

LIVE Challenge

With the popularity of smartphones and other camera devices in recent years, a vast amount of images have been produced and play an increasingly important role in people’s...

Dataset
JSON

PMT-IQA: Progressive Multi-Task Learning for Blind Image Quality Assessment

Blind image quality assessment (BIQA) remains challenging due to the diverse types of distortion and variable image content, which complicates the distortion patterns crossing...

Dataset
JSON

Blind CT Image Quality Assessment Using DDPM-Derived Content and Transformer-...

Blind CT image quality assessment using DDPM-derived content and transformer-based evaluator

Dataset
JSON

42 datasets found