CD-Net

Introduction

This repository contains the official pytorch implementation of the paper "Measuring Perceptual Color Differences of Smartphone Photographs" by Zhihua Wang, Keshuo Xu, Yang Yang, Jianlei Dong, Shuhang Gu, Lihao Xu, Yuming Fang and Kede Ma, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.

Measures for visual color differences (CDs) are pivotal in hardware and software upgrading of modern smartphone photographs. We firstly construct currently the largest database for visual CDs for smartphone photographs. We then conduct a large-scale psychophysical experiment to gather perceptual CDs of 30,000 image pairs in a carefully controlled laboratory environment. Based on the newly established dataset, we make one of the first attempts to construct an end-to-end learnable CD formula based on a lightweight neural network, as a generalization of several previous metrics. Extensive experiments demonstrate that the optimized formula outperforms 33 existing CD measures by a large margin, offers reasonable local CD maps without the use of dense supervision, generalizes well to homogeneous color patch data.

Dataset

Our database(SPCD) consists of 15335 natural images:

1. captured by six latest flagship smartphones
2. altered by Photoshop®
3. post-processed by built-in filters of smartphones
4. reproduced with incorrect color profiles

You can download the dataset via BaiduDisk or Google Drive. We also host a Community Prediction Competition about Visual Color Difference Evaluation.

Prerequisites

• python 3.10
• pytorch 1.12.0
• pip install -r requirement.txt

Usage

$ git clone https://github.com/hellooks/CDNet

Citation

If you find the repository helpful in your resarch, please cite the following papers.

@article{wang2022cdnet,
  author={Wang, Zhihua and Xu, Keshuo and Yang, Yang and Dong, Jianlei and Gu, Shuhang and Xu, Lihao and Fang, Yuming and Ma, Kede},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)}, 
  title={Measuring Perceptual Color Differences of Smartphone Photographs}, 
  year={2023},
  volume={},
  number={},
  doi={10.1109/TPAMI.2023.3262424}
  }

@inproceedings{xu2022database,
  title={A Database of Visual Color Differences of Modern Smartphone Photography},
  author={Xu, Keshuo and Wang, Zhihua and Yang, Yang and Dong, Jianlei and Xu, Lihao and Fang, Yuming and Ma, Kede},
  booktitle={2022 IEEE International Conference on Image Processing (ICIP)},
  pages={3758--3762},
  year={2022},
  organization={IEEE}
}