Loading [MathJax]/jax/output/SVG/config.js
Eurasian Journal of Mathematical and Computer Applications
RUS  ENG    ЖУРНАЛЫ   ПЕРСОНАЛИИ   ОРГАНИЗАЦИИ   КОНФЕРЕНЦИИ   СЕМИНАРЫ   ВИДЕОТЕКА   ПАКЕТ AMSBIB  
Общая информация
Последний выпуск
Архив

Поиск публикаций
Поиск ссылок

RSS
Последний выпуск
Текущие выпуски
Архивные выпуски
Что такое RSS



Eurasian Journal of Mathematical and Computer Applications:
Год:
Том:
Выпуск:
Страница:
Найти






Персональный вход:
Логин:
Пароль:
Запомнить пароль
Войти
Забыли пароль?
Регистрация


Eurasian Journal of Mathematical and Computer Applications, 2020, том 8, выпуск 1, страницы 76–90
DOI: https://doi.org/10.32523/2306-6172-2020-8-1-76-90
(Mi ejmca152)
 

Эта публикация цитируется в 25 научных статьях (всего в 25 статьях)

Comparative analysis of the SSIM index and the pearson coefficient as a criterion for image similarity

V. V. Starovoytova, E. E. Eldarovab, K. T. Iskakovb

a The State Scientific Institution «The United Institute of Informatics Problems of the National Academy of Sciences of Belarus»
b L.N.Gumilyov Eurasian National University, Nur-Sultan, Republic of Kazakhstan
Аннотация: In this paper,the SSIM index, which is the most popular measure of the structural image is studied. A mathematical proof that the SSIM index and its linear transformations are not metric functions is given. We demonstrated that this index, as well as any full-reference image comparison function, cannot evaluate the image quality. These functions estimate only some similarity degree between a reference image and its distorted copy. It is proved experimentally that the SSIM index does not always correctly determine similarity of images of the same scene. The Pearson linear correlation is a better tool for similarity analysis and it is faster to calculate. It is experimentally demonstrated that the Pearson correlation better than the SSIM index coincides with the subjective MOS image estimates. It is shown that the Pearson correlation coefficient is non-linearly related to the Euclid metric, but no any linear transformation of the coefficient can be a metric function. Our study proves that the Pearson correlation coefficient is superior to the SSIM index when evaluating image similarity.
Ключевые слова: Image similarity, Image quality, SSIM index, MOS, Metric, Pearson correlation.
Реферативные базы данных:
Тип публикации: Статья
MSC: 62H35
Язык публикации: английский
Образец цитирования: V. V. Starovoytov, E. E. Eldarova, K. T. Iskakov, “Comparative analysis of the SSIM index and the pearson coefficient as a criterion for image similarity”, Eurasian Journal of Mathematical and Computer Applications, 8:1 (2020), 76–90
Цитирование в формате AMSBIB
\RBibitem{StaEldIsk20}
\by V.~V.~Starovoytov, E.~E.~Eldarova, K.~T.~Iskakov
\paper Comparative analysis of the SSIM index and the pearson coefficient as a criterion for image similarity
\jour Eurasian Journal of Mathematical and Computer Applications
\yr 2020
\vol 8
\issue 1
\pages 76--90
\mathnet{http://mi.mathnet.ru/ejmca152}
\crossref{https://doi.org/10.32523/2306-6172-2020-8-1-76-90}
\isi{https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=Publons&SrcAuth=Publons_CEL&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=000519815300005}
\scopus{https://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-85084362371}
Образцы ссылок на эту страницу:
  • https://www.mathnet.ru/rus/ejmca152
  • https://www.mathnet.ru/rus/ejmca/v8/i1/p76
  • Эта публикация цитируется в следующих 25 статьяx:
    1. Omkulthoum G. Gadallah, Mohamed E. Nasr, Heba Ali Elkhobby, 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 2024, 1  crossref
    2. Ibrahim Ali Mohamed, Khaled Wassif, Hanaa Bayomi, 2024 6th International Conference on Computing and Informatics (ICCI), 2024, 345  crossref
    3. Maximilian Lipp, Wei Li, Ksenia Abrashitova, Patrick Forré, Lyubov V. Amitonova, “Lightweight super-resolution multimode fiber imaging with regularized linear regression”, Opt. Express, 32:9 (2024), 15147  crossref
    4. Sridevi Gamini, Kavya Sri Kamisetti, Priyanka Nallamilli, Sai Pavan Darisi, Venkatesh Venkatapathi, 2024 2nd International Conference on Networking and Communications (ICNWC), 2024, 1  crossref
    5. Maximilian Linde, Wolfram Wiest, Anna Trauth, Markus G. R. Sause, “Selecting Feasible Trajectories for Robot-Based X-ray Tomography by Varying Focus-Detector-Distance in Space Restricted Environments”, J Nondestruct Eval, 43:2 (2024)  crossref
    6. Valentin Michels, Chunwei Chou, Maximilian Weigand, Yuxin Wu, Andreas Kemna, “Quantitative phenotyping of crop roots with spectral electrical impedance tomography: a rhizotron study with optimized measurement design”, Plant Methods, 20:1 (2024)  crossref
    7. Lemin Shi, Xin Feng, Dianxin Song, Ping Gong, Ming Yue, Yuan Si, 2024 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO), 2024, 560  crossref
    8. Wei Lu, Gengsheng Ma, Chen Liu, Renxin Wang, Guojun Zhang, Wendong Zhang, Mehmet Yilmaz, Sai Zhang, “Developing 3D-CMUT for Ultrasonic Guided Wave-Based Damage Imaging Applications: A Comprehensive Theory and Simulation Study”, IEEE Sensors J., 24:24 (2024), 40494  crossref
    9. Nimmy George, Manju Manuel, Michael George, 2024 11th International Conference on Advances in Computing and Communications (ICACC), 2024, 1  crossref
    10. Wei Li, Ksenia Abrashitova, Lyubov V. Amitonova, “Super-resolution multimode fiber imaging with an untrained neural network”, Opt. Lett., 48:13 (2023), 3363  crossref
    11. Sergey V. Sai, Ekaterina S. Fomina, 2023 International Russian Automation Conference (RusAutoCon), 2023, 685  crossref
    12. Xiaodong YANG, Zhiyi MA, Yanlin REN, Meihui CHEN, Aijun HE, Jun WANG, “Multivariate emotional EEG signal recognition based on multivariate joint motif entropy of a horizontal visibility graph”, Sci. Sin.-Inf., 53:12 (2023), 2406  crossref
    13. Peter Schier, Aaron Jaufenthaler, Maik Liebl, Soudabeh Arsalani, Frank Wiekhorst, Daniel Baumgarten, “Human-sized quantitative imaging of magnetic nanoparticles with nonlinear magnetorelaxometry”, Phys. Med. Biol., 68:15 (2023), 155002  crossref
    14. Arpita Nema, Vinit Jakhetiya, Sunil Jaiswal, Subudhi Badri, Sharath Chandra Guntuku, 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), 2023, 1  crossref
    15. Geunho Jung, Semin Kim, Jongha Lee, Sangwook Yoo, “Deep learning-based optical approach for skin analysis of melanin and hemoglobin distribution”, J. Biomed. Opt., 28:03 (2023)  crossref
    16. Esraa Khalid Ahmed Alobaydi, Omar Muayad Abdullah, 2022 8th International Conference on Contemporary Information Technology and Mathematics (ICCITM), 2022, 347  crossref
    17. Peng Lin, Chuan Li, Andres Flores-Valle, Zian Wang, Meng Zhang, Ran Cheng, Ji-Xin Cheng, “Tilt-angle stimulated Raman projection tomography”, Opt. Express, 30:20 (2022), 37112  crossref
    18. Ming Li, Hui Li, “Application of Deep Convolutional Neural Network Under Region Proposal Network in Patent Graphic Recognition and Retrieval”, IEEE Access, 10 (2022), 37829  crossref
    19. Miho Morita, Mai Horitsuji, “Aging-Related Alteration in Cell Size Distribution in the Stratum Corneum and Its Relationship with Facial Appearance”, Jouranal of Society Cosmetic Chemists Japan, 56:2 (2022), 121  crossref
    20. Hao Shi, Qinglong Tang, Kalim Uddeen, Gaetano Magnotti, James Turner, “A New Method to Measure the Spatial Distribution of Pressure Oscillations in Engine Knock Using Optical Diagnostics”, SSRN Journal, 2022  crossref
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    Eurasian Journal of Mathematical and Computer Applications
    Статистика просмотров:
    Страница аннотации:916
    PDF полного текста:618
    Список литературы:1
     
      Обратная связь:
    math-net2025_02@mi-ras.ru
     Пользовательское соглашение  Регистрация посетителей портала  Логотипы © Математический институт им. В. А. Стеклова РАН, 2025