docs/research/08_quality.json

[
  {
    "url": "https://github.com/chaofengc/iqa-pytorch",
    "name": "GitHub - chaofengc/IQA-PyTorch: 🖼️ PyTorch Toolbox for Image Quality",
    "snippet": "This is a comprehensive image quality assessment (IQA) toolbox built with pure Python and PyTorch. We provide reimplementation of many widely used full ...",
    "host_name": "github.com",
    "rank": 0,
    "date": "",
    "favicon": ""
  },
  {
    "url": "https://github.com/topics/blind-image-quality-assessment",
    "name": "blind-image-quality-assessment · GitHub Topics",
    "snippet": "Toolbox for Image Quality Assessment, including PSNR, SSIM, LPIPS, FID, NIMA, DBCNN, BRISQUE, PI and more... A comprehensive collection of IQA papers awesome",
    "host_name": "github.com",
    "rank": 1,
    "date": "",
    "favicon": ""
  },
  {
    "url": "https://github.com/tazztone/ComfyUI-Image-Quality-Assessment",
    "name": "GitHub - tazztone/ComfyUI-Image-Quality",
    "snippet": "A comprehensive Image Quality Assessment (IQA) custom node collection for ComfyUI. Combines deep learning-based metrics (PyIQA), classical computer vision ...",
    "host_name": "github.com",
    "rank": 2,
    "date": "",
    "favicon": ""
  },
  {
    "url": "https://csyhquan.github.io/manuscript/22-mm-No-Reference%20Image%20Quality%20Assessment%20Using%20Dynamic%20Complex-Valued%20Neural%20Model.pdf",
    "name": "[PDF] No-Reference Image Quality Assessment Using Dynamic",
    "snippet": "Image quality assessment (IQA) aims at quantifying human percep- tion of image quality. is about estimating the perceived quality of a distorted image without ...",
    "host_name": "csyhquan.github.io",
    "rank": 3,
    "date": "",
    "favicon": ""
  },
  {
    "url": "https://github.com/idealo/image-quality-assessment",
    "name": "idealo/image-quality-assessment - GitHub",
    "snippet": "This repository provides an implementation of an aesthetic and technical image quality model based on Google's research paper \"NIMA: Neural Image Assessment\".",
    "host_name": "github.com",
    "rank": 4,
    "date": "",
    "favicon": ""
  },
  {
    "url": "https://57blocks.com/blog/image-quality-assessment-using-machine-learning",
    "name": "Image Quality Assessment Using Machine Learning - 57Blocks",
    "snippet": "Google's Neural Image Assessment (NIMA): NIMA is another tool for assessing image quality. It uses a convolutional neural network to predict ...",
    "host_name": "57blocks.com",
    "rank": 5,
    "date": "",
    "favicon": ""
  },
  {
    "url": "https://github.com/chaofengc/Awesome-Image-Quality-Assessment",
    "name": "Awesome Image Quality Assessment (IQA) - GitHub",
    "snippet": "A comprehensive collection of IQA papers, datasets and codes. We also provide PyTorch implementations of mainstream metrics in IQA-PyTorch toolbox.",
    "host_name": "github.com",
    "rank": 6,
    "date": "",
    "favicon": ""
  },
  {
    "url": "https://github.com/topics/image-quality-assessment",
    "name": "image-quality-assessment · GitHub Topics",
    "snippet": "Image quality is an open source software library for Image Quality Assessment (IQA). python machine-learning computer-vision tensorflow artificial ...",
    "host_name": "github.com",
    "rank": 7,
    "date": "",
    "favicon": ""
  },
  {
    "url": "https://arxiv.org/html/2502.08540v1",
    "name": "A Survey on Image Quality Assessment: Insights, Analysis, and",
    "snippet": "This survey delivers an extensive analysis of contemporary IQA methodologies, organized according to their application scenarios.",
    "host_name": "arxiv.org",
    "rank": 8,
    "date": "",
    "favicon": ""
  },
  {
    "url": "https://medium.com/data-science/deep-image-quality-assessment-with-tensorflow-2-0-69ed8c32f195",
    "name": "Deep CNN-Based Blind Image Quality Predictor in Python - Medium",
    "snippet": "In this tutorial, we will implement the Deep CNN-Based Blind Image Quality Predictor (DIQA) methodology proposed by Jongio Kim, ...",
    "host_name": "medium.com",
    "rank": 9,
    "date": "",
    "favicon": ""
  }
]