CURD-BIQA
conda create -n curd python=3.8.18
conda activate curdbiqa
pip install -r requirements.txt
python framework.py <--curd> --method <method> --dataset <dataset> --index <indexs> --beta <betas>
Some available options:
--dataset
: Testing dataset, support datasets: koniq-10k | live | csiq | tid2013.--method
: basic method.--curd
: The flag of using curd (Using multiscale framework), switch off represents the original method.--index
: The curd selected indexs, only predicting process used.--beta
: The curd selected betas, only predicting process used.
Outputs:
orignal mehthod and predicting processing
: printPLCC
andSRCC
Usage of curd, get the layer scores
: .\outputs\<method>\ multiscale outputs\<dataset>.txt
python curd_iqa.py --method <method> --inputFileSet <input file names> --outputFile <output file name> --save_num <save_num>
Some available options:
--method
: basic method.--inputFileSet
: input file names in multiscale outputs folder, support names: koniq-10k.txt | live.txt | csiq.txt | tid2013.txt.--outputFile
: output file names.--save_num
: Save numbers, default number is 5000000.
Outputs:
output file
: .\outputs\<method>\curd outputs\<outputFile>.txt