FPR

Code for paper "No-Reference Image Quality Assessment by Hallucinating Pristine Features".

Environment

• python=3.8.5
• pytorch=1.7.1 cuda=11.0.221 cudnn=8.0.5_0

Running

• Data Prepare

• Download the natural image (NI) datasets and screen content image (SCI) datasets into the path: ./FPR/datasets/
• We provide the pretrained checkpoints here. You can download it and put the included files into the path: ./FPR/FPR_IQA/FPR_NI/models/" or "./FPR/FPR_IQA/FPR_SCI/models/.

• Train:

  • For NI:
    python ./FPR/FPR_IQA/FPR_SCI/src/iqaScrach.py --list-dir='../scripts/dataset_name/' --resume='../models/model_files/checkpoint_latest.pkl' --pro=split_id --dataset='dataloader_name'
    • dataset_name: "tid2013", "databaserelease2", "CSIQ", or "kadid10k"
    • model_files: "tid2013", "live", "csiq", or "kadid"
    • dataloader_name: "IQA" (for live and csiq datasets), "TID2013", or "KADID"
    • split_id: '0' to '9'
  • For SCI:
    • SIQAD: python ./FPR/FPR_IQA/FPR_SCI/src/iqaScrach.py --pro=split_id
    • SCID: python ./FPR/FPR_IQA/FPR_SCI/src/scid-iqaScrach.py --pro=split_id

• Test:

  • For NI:
    python ./FPR/FPR_IQA/FPR_SCI/src/iqaTest.py --list-dir='../scripts/dataset_name/' --resume='../models/model_files/model_best.pkl' --pro=split_id --dataset='dataloader_name'
  • For SCI:
    • SIQAD: python ./FPR/FPR_IQA/FPR_SCI/src/iqaTest.py --pro=split_id
    • SCID: python ./FPR/FPR_IQA/FPR_SCI/src/scid-iqaTest.py --pro=split_id

Details

• Waitting...