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Image Quality Assesment ( with Demo )

This shows the implementation of Image Quality Assesment using DNN with No Reference.

I prepared training/test/demo system for IQA. In demo, this system captures image by web camera and outputs IQA score in real-time.

You can try it on your local PC as well as goole colaboratory. But Trainng is possible only on your PC.
(Training on google colab is in the future.)

[Reference Paper]
will appear soon

Overview

Network Model

This method is based on Convolutional Neural Network (CNN). The structure is shown in the following figure. In this system, the output value takes continuous values. Several convolutional layers have a role in extracting the underlying image features. Structure

  • Activation Function : Parametric ReLU (Output : Linear)

Notice : Actually, my network model described in demo_main.py is a little different with the abobe one for more improvement.

Training Parameters

The parameters for training are the followings.

  • Training Dataset : TID2013 color images
  • Loss Function : Mean Squared Error
  • Updating Algorithm : AMSBound
  • Learning Rate : 0.00005
  • Batch Size : 100
  • Epoch : 5000

You can change those parameters from settings.py.

Demo

Equipment

  • Web Camera
    You need web camera to capture photo or video as the input.

How to Execute

You can choose from two ways to execute DNN-based IQA, on Google Colaboratory or your Local PC.

1. Google Colaboratory : for only Demo (one-shot).

2. Local PC : for Training / Test / Demo (continuous).

If you want to run Only Demo, I recommend you to use Google Colaboratory.

1. On Google Colaboratory

You can try demo on goole colaboratory. Google colab is a very conbenient platform because all the processes are done on the cloud server. I show the procedure to execute.

Demo

  1. Access IQA_demo.ipynb and push Open In Colab bottun. Or access this link directly. After log-in Google, you can automatically open source code for demo.

  2. Before Running, connect web camera to your device.

  3. Run all cells. Captured video is displayed.

  4. Push 'Capture' bottun on the console. The system executes one-shot evaluation and then displays the IQA result on the console with the captured picture.
    demo1

Notice : If not working, you may have to allow the permission of your browser to access the web camera.

2. On local PC

You can also demo on your local environment. But in this case you have to install some python pachages. Please follow the below.

Requirements

Python & CUDA

Python Package

At the biggining, I reccomend you to upgrade pip in command prompt by

$ pip install --upgrade pip

If you get Access Denied Error by the above, try

$ pip install --upgrade pip --user

and then install the following packages.

  • numpy
  • nnabla (>=1.0.20)
  • nnala-ext-cuda (>=1.0.20)
    • Install appropriate CUDA and CuDNN in your PC. If you use nnabla-ext-cuda 1.0.20, install CUDA 10.1 and cuDNN 7.6 from NVIDIA. Read more.
  • joblib
  • cv2
    • You can install it by the following command.
    $ pip install opencv-python

Run ( Training / Test / Demo )

Download all files and run demo_main.py.
According to your purpose, change the following two variables in demo_main.py appropriately.

Parameters for Training for Test for Demo
Demo False False True
Test False True True/False

For example, If you want to execute training, please set

 Demo = False         
 Test = False

Demo

  1. Run demo_main.py with demo mode (see above).

  2. You can find the continuous IQA score (0% - 100%) in real-time on the display window.

  3. If you need, use the following commmands during running:

    • v: Start Video Recording
      If you press v key, you can start to record the display window as "mp4" video.

    • t: Stop Video Recording
      If you press t key, you can stop to record the display window as "mp4" video.
      You can find the saved video.mp4 file in result folder after stop.

    • q: Exit
      If you press q key, you can exit.

demo2

Notice : You can see the demo video from here.

Before Training

  1. Make folders data/image_train/ and data/image_test/ in the same directory with source codes.

  2. Download TID2013 dataset from site(Direct link).
    TAMPERE IMAGE DATABASE 2013 TID2013, version 1.0
    http://www.ponomarenko.info/tid2013.htm

  3. Unzip tid2013.tar.

  4. Resize all images to 64 x 64.

  5. Choose somes image files from tid2013/distorted_images/ as train data and move them to data/image_train/.

  6. Copy mos.txt in tid2013/ and paste to data/image_train/, and Remove some values which is not corresponding to the selected train data.

  7. Do 5. and 6. again for test data.

  8. Run demo_main.py with Train mode.

Notice

Trained Network Model

The trained network is already stored in params folder ( You can see from here. ). By default, you do not need to execute training.

If you want to use the trained network, open settings.py and please set

self.epoch = 5000

Fine Tuning

  1. In demo, IQA value is very sensitive to the out of focus.
    If you want to gain the score in demo, adjust camera focus or change the distance between the camera and the object.

  2. Inversely, when you add a blur, the IQA socre is obviously degradaded.