MB-CNN

PyTorch implementation of the following paper: Z. Pan, F. Yuan, X. Wang, L. Xu, S. Xiao and S. Kwong, "No-Reference Image Quality Assessment via Multi-Branch Convolutional Neural Networks," in IEEE Transactions on Artificial Intelligence, doi: 10.1109/TAI.2022.3146804.

Note

• The optimizer is chosen as Adam here, instead of the SGD with momentum in the paper.
• the mat files in data/ are the information extracted from the datasets and the index information about the train/val/test split. The subjective scores of LIVE is from the realigned data.

Training

You can get the dataset from https://pan.baidu.com/s/18y5zswF_rKaQbf0ZPuP5Bg [1ih5] , and then put them into "data/".

CUDA_VISIBLE_DEVICES=0 python main.py --exp_id=0 --database=LIVE

Before training, the im_dir in config.yaml must to be specified. Train/Val/Test split ratio in intra-database experiments can be set in config.yaml (default is 0.6/0.2/0.2).

Visualization

tensorboard --logdir=tensorboard_logs --port=6006 # in the server
ssh -L 6006:localhost:6006 user@host # in your PC, then see the visualization in your PC