A blind image evaluator based on a convolutional neural network (BIECON) is a no-reference image quality assessment method using a CNN. This code implements the system described in the following paper:
J. Kim and S. Lee, “Fully deep blind image quality predictor,” IEEE Journal of Selected Topics in Signal Processing, vol. 11, no. 1, pp. 206–220, Feb. 2017.
This code was developed and tested with Theano 0.9, CUDA 8.0, and Windows.
- Set
BASE_PATH
to the actual root path of each database. SetFR_MET_BASEPATH
andFR_MET_SUBPATH
ingen_local_metric_scores.m
. - For each database, data will be stored in "
FR_MET_BASEPATH
+FR_MET_SUBPATH
". - Run
gen_local_metric_scores.m
using Matlab. We provide a SSIM metric as default.
For each database, set BASE_PATH
to the actual root path of each database in the following files:
IQA_BIECON_release/data_load/LIVE.py
,
IQA_BIECON_release/data_load/TID2008.py
, and
IQA_BIECON_release/data_load/TID2013.py
.
(These BASE_PATH
should be same to the BASE_PATH
in gen_local_metric_scores.m
.)
Set FR_MET_BASEPATH
and FR_MET_SUBPATH_{DB name}
in
IQA_BIECON_release/data_load/data_loader_IQA.py
.
{DB name} can be LIVE, TID2008, or TID2013
(These should be same to those in gen_local_metric_scores.m
.)
Detailed configuration of local quality score maps is set in NR_biecon.yaml
.
fr_met
: This describes the name of the full-reference image quality assessment metric. The corresponding local quality score maps must be generated first. ex) SSIM, FSIM ...fr_met_scale
: This indicates the scale ratio of the local quality score maps to their original images. This should be same toimage_resize
ingen_local_metric_scores.m
.fr_met_avg
: If True, for each divided image patch, local quality score maps are averaged to be scalar values. Otherwise, patch of local quality score maps are used. This is used to reduce the usage of memory.
We provide the demo code for training a BIECON model.
python example.py
tr_te_file
: Store the randomly divided (training and testing) reference image indices in this file.snap_path
: This indicates the path to store snapshot files
BIECON was tested on the full-sets of LIVE IQA, TID2013, and CSIQ databases. During the experiment, we randomly divided the reference images into two subsets, 80% for training and 20% for testing. The correlation coefficients were averaged after the procedure was repeated 10 times while dividing the training and testing sets randomly.
Database | SRCC | PLCC |
---|---|---|
LIVE | 0.9603 | 0.9622 |
TID2013 | 0.7205 | 0.7650 |
CSIQ | 0.8251 | 0.8380 |