# IQA02

**Repository Path**: fl9621/IQA02

## Basic Information

- **Project Name**: IQA02
- **Description**: No description available
- **Primary Language**: Matlab
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-05-11
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?
[![License](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](License)

## Description
SFA code for the following papers:

- Li, Dingquan, Tingting Jiang, Weisi Lin, and Ming Jiang. "[Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?](https://ieeexplore.ieee.org/document/8489929)." IEEE Transactions on Multimedia, vol. 21, no. 5, pp. 1221-1234, May 2019.
- Li, Dingquan, Tingting Jiang, and Ming Jiang. "[Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images](https://dl.acm.org/citation.cfm?id=3123266.3123322)." Proceedings of the 2017 ACM on Multimedia Conference. ACM, 2017.

## Requirement
Framework: [Caffe](https://github.com/BVLC/caffe/) 1.0 + [MATLAB](https://www.mathworks.com/products/matlab.html) 2016b Interface

The PLSR model uesd in the test code is trained on [LIVE](http://live.ece.utexas.edu/research/Quality/subjective.htm) gblur images with DMOS (the larger the worse). `w` and `best_layer` in the journal extension are determined by five cross-validation (See `TMMinter.m`).

The `ResNet-50-model.caffemodel` is downloaded from [KaimingHe/deep-residual-networks](https://github.com/KaimingHe/deep-residual-networks) and it should be pasted into the directory `models/` before you run the code! 
It's about 100MB which is too large to upload to this repo.
If you have difficulty, you can also download the `ResNet-50-model.caffemodel` in [my sharing on BaiduNetDisk](https://pan.baidu.com/s/1T32sYjrQA04kl1auArirxw) with password `u8sd`.

**New!** We provide the [PyTorch](https://pytorch.org) implementation of the method in [SFA-pytorch](./SFA-pytorch/)

## Notes
### Note for training
All we need to train is a PLSR model, where the training function is `plsregress` in [MATLAB](https://www.mathworks.com/products/matlab.html). The features are extracted from the DCNN models pre-trained on the image classification task.

Update: remember to change the value of "im_dir" and "im_lists" in data info.

### Note for datasets
You can download the datasets used in the papers from their owners for research purpose. If you have difficulty, you can refer to [my sharing on BaiduNetDisk](https://pan.baidu.com/s/10rGDziwuQl0fjwDHlhX1dA) with password `b6sv`. We only consider the blur related images in this work.

### Note for cross dataset evaluation
The reported Spearman correlation (SROCC) is multiplied by `-1` when the training and testing datasets have different forms of subjective scores, i.e., one is MOS and the other is DMOS. This is to make sure that the prediction monotonicity is better when SROCC is closer to 1.

## Citation

Please cite our papers if it helps your research:

<pre><code>@arcticle{li2018which,
  title={Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?},
  author={Li, Dingquan and Jiang, Tingting and Lin, Weisi and Jiang, Ming},
  booktitle={IEEE Transactions on Multimedia},
  volume={21}, 
  number={5}, 
  pages={1221-1234},  
  month={May},
  year={2019}, 
  doi={10.1109/TMM.2018.2875354}
}</code></pre>

[[Paper](https://ieeexplore.ieee.org/document/8489929)] 

<pre><code>@inproceedings{li2017exploiting,
  title={Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images},
  author={Li, Dingquan and Jiang, Tingting and Jiang, Ming},
  booktitle={Proceedings of the 2017 ACM on Multimedia Conference},
  pages={378--386},
  year={2017},
  organization={ACM}
}</code></pre>

[[Paper](https://www.researchgate.net/profile/Dingquan_Li3/publication/320541334_Exploiting_High-Level_Semantics_for_No-Reference_Image_Quality_Assessment_of_Realistic_Blur_Images/links/5a0c14cba6fdccc69edaa035/Exploiting-High-Level-Semantics-for-No-Reference-Image-Quality-Assessment-of-Realistic-Blur-Images.pdf)] 
[[Poster](./acmmm17_poster-updated.pdf)]

## Contact
Dingquan Li, dingquanli AT pku DOT edu DOT cn.