This software provides computations of the following objective metrics on the CSIQ image database [1]: MSE, SNR, PSNR, PSNR-HVS, PSNR-HVS-M, UQI, SSIM, MS-SSIM, M-SVD, QILV, IFC, VIF, VIFp, FSIM, IW-MSE, IW-PSNR, IW-SSIM, WSNR, VSNR, DN. Codes are obtained from implementations within the references in the end of this document.
The software also plots the results (1-DMOS) against subjective scores for comparison, and computes the Pearson Correlation Coefficient between objective results and subjective ratings in CSIQ database.
Plots are saved in the same folder as .png files.
PSNR-HVS, PSNR-HVS-M and VIFp results are computed using the VQMT software [2]. The OpenCV library (http://opencv.willowgarage.com/wiki/) needs to be installed to run VQMT. Only the core and imgproc modules are required.
MATLAB (versions since 2013)
Download all folders and files in https://drive.google.com/drive/folders/13KRftoy8b38uUBx9J338gG5R_Deymix0?usp=sharing to local folder.
Call computeObjective.m in MATLAB.
VQM and VMAF metrics are computed on the CSIQ video database [13].
VQM is computed using VQM software available at https://www.its.bldrdoc.gov/resources/video-quality-research/guides-and-tutorials/description-of-vqm-tools.aspx.
VMAF is computed using VMAF softwate available at https://github.com/Netflix/vmaf In case of installation problems, use the Python independent vmafossexec build. Example commads for running VMAF on the CSIQ video database are provided in VMAFcommands.txt
[1] CSIQ Image Quality Database, vision.eng.shizuoka.ac.jp/mod/page/view.php?id=23. [2] https://mmspg.epfl.ch/vqmt [3] Zhou, Wang and Alan C. Bovik. “A Universal Image Quality Index.” IEEE Signal Processing Letters 9.3 (2002): 81-84. [4] Aja-Fernandez, Santiago, et al. "Image quality assessment based on local variance." Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE. IEEE, 2006. [5] Sheikh, Hamid R., Alan C. Bovik, and Gustavo De Veciana. "An information fidelity criterion for image quality assessment using natural scene statistics." IEEE Transactions on image processing 14.12 (2005): 2117-2128. [6] H.R. Sheikh.and A.C. Bovik, "Image information and visual quality," IEEE Transactions on Image Processing , vol.15, no.2,pp. 430- 444, Feb. 2006. [7] Zhang, Lin, et al. "FSIM: A feature similarity index for image quality assessment." IEEE transactions on Image Processing20.8 (2011): 2378-2386. [8] Wang, Zhou, and Qiang Li. "Information content weighting for perceptual image quality assessment." IEEE Transactions on Image Processing 20.5 (2011): 1185-1198. [9] Mannos, James, and David Sakrison. "The effects of a visual fidelity criterion of the encoding of images." IEEE transactions on Information Theory 20.4 (1974): 525-536. [10] Mitsa, Theophano, and Krishna Lata Varkur. "Evaluation of contrast sensitivity functions for the formulation of quality measures incorporated in halftoning algorithms." Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on. Vol. 5. IEEE, 1993. [11] MeTRriX MuX Visual Quality Assessment Package V1.1. https://github.com/sattarab/image-quality-tools. Originally developed at http://foulard.ece.cornell.edu/gaubatz/metrix_mux/ [12] Laparra, Valero, Jordi Muñoz-Marí, and Jesús Malo. "Divisive normalization image quality metric revisited." JOSA A 27.4 (2010): 852-864. [13] CSIQ Video Quality Database, http://vision.eng.shizuoka.ac.jp/mod/page/view.php?id=24.