Skip to content

Latest commit

 

History

History
31 lines (21 loc) · 2.4 KB

README.md

File metadata and controls

31 lines (21 loc) · 2.4 KB
Raw

Introduction

This repository contain the source code of the following technical report:

@article{CLRIQA,
  author    = {Fu-Zhao {Ou} and
               Yuan-Gen {Wang} and
               Jin {Li} and
               Guopu {Zhu} and
               Sam {Kwong}},
  title     = {Controllable List-wise Ranking for Universal No-reference Image Quality Assessment},
  journal   = {arXiv preprint arXiv:1911.10566},
  year      = {2019},
}

Abstract

No-reference image quality assessment (NR-IQA) has received increasing attention in the IQA community since reference image is not always available. Real-world images generally suffer from various types of distortion. Unfortunately, existing NR-IQA methods do not work with all types of distortion. It is a challenging task to develop universal NR-IQA that has the ability of evaluating all types of distorted images. In this paper, we propose a universal NR-IQA method based on controllable list-wise ranking (CLRIQA). First, to extend the authentically distorted image dataset, we present an imaging heuristic approach, in which the over-underexposure is formulated as an inverse of Weber-Fechner law, and fusion strategy and probabilistic compression are adopted, to generate the degraded real-world images. These degraded images are label-free yet associated with quality ranking information. We then design a controllable list-wise ranking function by limiting rank range and introducing an adaptive margin to tune rank interval. Finally, the extended dataset and controllable list-wise ranking function are used to pre-train a CNN. Moreover, in order to obtain an accurate prediction model, we take advantage of the original dataset to further fine-tune the pre-trained network. Experiments evaluated on four benchmark datasets (i.e. LIVE, CSIQ, TID2013, and LIVE-C) show that the proposed CLRIQA improves the state of the art by over 9% in terms of overall performance.

Framework

All of training and testing operations are run in Caffe framework.

Models

The VGG-16 on ImageNet model can be downloaded.

In addition, our trained CNN models will be uploaded as soon as possible at Baidu and Google cloud.

License

We utilize the Caffe framework and VGG-16. Please check their licence files for details. Moreover, this source code is made available for research purpose only.