ЕФЕКТИВНІ МЕТОДИ ІНТЕЛЕКТУАЛЬНОЇ ОБРОБКИ ЗОБРАЖЕНЬ ТА ВІДЕО НА ОСНОВІ МЕТРИК ВІЗУАЛЬНОЇ ЯКОСТІ ДЛЯ ПЕРСПЕКТИВНИХ ЗАСТОСУВАНЬ

Автор(и)

  • Фангфанг Лі Національний аерокосмічний університет ім. М.Є. Жуковського «Харківський авіаційний інститут»
  • Володимир Лукін Національний аерокосмічний університет ім. М.Є. Жуковського «Харківський авіаційний інститут» https://orcid.org/0000-0002-1443-9685
  • Сергій Абрамов Національний аерокосмічний університет ім. М.Є. Жуковського «Харківський авіаційний інститут» https://orcid.org/0000-0002-8295-9439
  • Андрій Рубель Національний аерокосмічний університет ім. М.Є. Жуковського «Харківський авіаційний інститут» https://orcid.org/0000-0003-0724-6727
  • Кшиштоф Окарма Західнопоморський технологічний університет в Щецині https://orcid.org/0000-0002-6721-3241
  • Пьотр Лєх Західнопоморський технологічний університет в Щецині https://orcid.org/0000-0003-2554-7582
  • Войцех Хлевицьки Західнопоморський технологічний університет в Щецині https://orcid.org/0000-0002-9564-3062
  • Матеуш Копитек Західнопоморський технологічний університет в Щецині

DOI:

https://doi.org/10.30890/2709-2313.2023-21-01-012

Ключові слова:

Visual quality, combined metrics, remote sensing, multiple distortions

Анотація

This chapter contains the results obtained during execution of Ukrainian-Polish Project in 2021 intended on design of methods and means for processing grayscale and multichannel images and video using visual quality metrics. The combined metrics have been

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Посилання

Okarma, K. Combined Full-Reference Image Quality Metrics for Objective Assessment of Multiply Distorted Images /K. Okarma, P. Lech, V.V. Lukin// Electronics 2021. – No. 10. – P. 2256. − https://doi.org/10.3390/ electronics10182256M.

Li, F. Quality control for the BPG lossy compression of three-channel remote sensing images (manuscript ID INFOR2109-020) /F. Li, K. Okarma, V. Lukin// Submitted to Informatica, 2021.

Entropy-based Combined Metric for Automatic Objective Quality Assessment of Stitched Panoramic Images /K. Okarma, W. Chlewicki, M. Kopytek, B. Marciniak, V. Lukin// Submitted to MDPI Entropy, 2021.

Intelligent lossy compression method of providing a desired visual quality for images of different complexity /F. Li, V. Lukin, K. Okarma, Y. Fu, J. Duan// Proceedings of 2021 International Conference on Applied Mathematics, Modeling and Computer Simulation. – China. − Nov. 2021. − 534 p.

Big data for remote sensing: Challenges and opportunities / M. Chi, A. Plaza, J. A. Benediktsson, Z. Sun, J. Shen, and Y. Zhu // Proceedings of the IEEE. − 2016. − Vol. 104, No. 11. − P. 2207-2219.

Remote sensing big data computing: Challenges and opportunities / Y. Ma et al. // Future Generation Computer Systems. − 2015. − Vol. 51. − P. 47-60.

Hyperspectral remote sensing data analysis and future challenges /J. M. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. Nasrabadi, J. Chanussot// IEEE Geoscience remote sensing magazine. − 2013. − Vol. 1, No. 2. − P. 6-36.

Blanes, I. A tutorial on image compression for optical space imaging systems /I. Blanes, E. Magli, J. Serra-Sagrista// IEEE Geoscience Remote Sensing Magazine. − 2014. − Vol. 2, No. 3. − P. 8-26.

Manolakis, D. On the spectral correlation structure of hyperspectral imaging data /D. Manolakis, R. Lockwood, T. Cooley// 2008-2008 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). − 2008. − Boston, MA, USA, IEEE. − Vol. 2. − P. II-581-II-584.

Christophe, E. Hyperspectral data compression tradeoff /S. Prasad, L. M. Bruce, J. Chanussot, Eds.// Optical remote sensing. − 2011. − Berlin, Heidelberg: Springer, 2011. − P. 9-29.

Zhang, X. Lossy compression and iterative reconstruction for encrypted image /X. Zhang// IEEE transactions on information forensics security. −2011. − Vol. 6, No. 1. − P. 53-58.

Quality controlled ECG data compression based on 2D discrete cosine coefficient filtering and iterative JPEG2000 encoding /A. Pandey, B.S. Saini, B. Singh, N.J.M. Sood// 2020. − Vol. 152. − P. 107252.

Li, F. A Two-step Approach to Providing a Desired Visual Quality in Image Lossy Compression /F. Li, S. Krivenko, V. Lukin// 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). – 2020. −

P. 502-506.

Li, F. Adaptive two-step procedure of providing desired visual quality of compressed image / F. Li, S. Krivenko, V. Lukin// Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering. – 2020, − P. 407-414.

Li, F. Analysis of two-step approach for compressing texture images with desired quality /F. Li, S. Krivenko, V. Lukin// Aerospace Technic and Technology.  2020.  Vol. 161, No. 1.  P. 50-58.

Li, F. A Two-step Procedure for Image Lossy Compression by ADCTC With a Desired Quality /F. Li, S. Krivenko, V. Lukin // Proceedings of 2020 IEEE 11th International Conference on Dependable Systems, Services and Technologies (DESSERT).  2020.  P. 307-312.

Li, F. Two-step providing of desird quality in lossy image compression by SPIHT /F. Li, S. Krivenko, V. Lukin// Radioelectronic and computer systems.  2020.  Vol. 94, No. 2.  P. 22-32.

Ponomarenko, N. AGU download page.  2008.  Available: http://ponomarenko.info/agu.htm.

Okarma, K. Improved quality assessment of colour surfaces for additive manufacturing based on image entropy /K. Okarma, J. Fastowicz// Pattern Analysis Applications.  2020.  Vol. 23, No. 3.  P. 1035-1047.

Dua, Y. Comprehensive review of hyperspectral image compression algorithms /Y. Dua, V. Kumar, R. S. Singh // Optical Engineering.  2020.  Vol. 59, No. 9.  P. 090902.

Du, Q. Hyperspectral image compression using JPEG2000 and principal component analysis /Q. Du, J. E. Fowler// IEEE Geoscience Remote sensing letters.  2007.  Vol. 4, No. 2.  P. 201-205.

Smart lossy compression of images based on distortion prediction /S. Krivenko, O. Krylova, E. Bataeva, V. Lukin// Telecommunications and Radio Engineering.  2018.  Vol. 77, No. 17.

Full-Reference Quality Metric Based on Neural Network to Assess the Visual Quality of Remote Sensing Images /O. Ieremeiev, V. Lukin, K. Okarma, K. Egiazarian// Geosciences Remote Sensin.  2020.  Vol. 12, No. 15.  P. 2349.

Yu, G. Image compression systems on board satellites /G. Yu, T. Vladimirova, M. N. Sweeting // Acta Astronautica.  2009.  Vol. 64, No. 9-10.  P. 988-1005.

Dellepiane, S. G. Quality assessment of despeckled SAR images /S. G. Dellepiane, E. Angiati// IEEE Journal of Selected Topics in Applied Earth Observations Remote Sensing.  2013.  Vol. 7, No. 2.  P. 691-707.

Perceptual quality assessment of pan-sharpened images /O. A. Agudelo-Medina, H. D. Benitez-Restrepo, G. Vivone, A. Bovik // Remote Sensing.  2019.  Vol. 11, No. 7.  877 p.

Mean deviation similarity index: Efficient and reliable full-reference image quality evaluator /H. Z. Nafchi, A. Shahkolaei, R. Hedjam, M. Cheriet// IEEE Access.  Vol. 4.  557 p.

Ozah, N. Compression improves image classification accuracy / N. Ozah, A. Kolokolova// Canadian Conference on Artificial Intelligence.  Springer, 2016.  Vol. 9-5590.  P. 525-530.

Chen, Z. Effects of compression on remote sensing image classification based on fractal analysis /Z. Chen, Y. Hu, Y. Zhang// IEEE Transactions on Geoscience Remote Sensing.  2019.  Vol. 57, No. 7.  P. 4577-4590.

Effects of JPEG and JPEG2000 lossy compression on remote sensing image classification for mapping crops and forest areas /A. Zabala, X. Pons, R. Díaz-Delgado, F. García, F. Aulí-Llinàs, J. Serra-Sagristà// Proceedings of 2006 IEEE International Symposium on Geoscience and Remote Sensing.  IEEE.  2006.  P. 790-793.

Lossy Compression of Multichannel Remote Sensing Images with Quality Control /V. Lukin et al. // Remote Sensing.  2020.  Vol. 12, No. 22. 

P. 3840.

Still image/video frame lossy compression providing a desired visual quality /A. Zemliachenko, V. Lukin, N. Ponomarenko, K. Egiazarian, J. Astola // Multidimensional Systems Signal Processing: Image Communicatio.  2016.  Vol. 27, No. 3.  P. 697-718.

Bellard, F. (2018, 2021.7.25). BPG Image format. Available: https://bellard.org/bpg/.

Ponomarenko, N. (2009, 2021.6.30). PSNR-HVS-M download page. Available: http://www.ponomarenko.info/psnrhvsm.htm.

Image database TID2013: Peculiarities, results and perspectives /N. Ponomarenko et al. // Signal processing: Image communication.  2015.  Vol. 30.  P. 57-77.

A challenge to parse the earth through satellite images / I. Demiret al.  2018.  https://arxiv.org/abs/1805.06561.

Chitade, A. Z. Colour based image segmentation using k-means clustering / A. Z. Chitade, S. Katiyar// International Journal of Engineering Science Technology.  2010.  Vol. 2, No. 10.  P. 5319-5325.

Assessment of the Segmentation of RGB Remote Sensing Images: A Subjective Approach /G. Kazakeviciute-Januskeviciene, E. Janusonis, R. Bausys, T. Limba, M. Kiskis// Remote Sensing.  2020.  Vol. 12, No. 24.  4152 p.

Kozhemiakin, R. Image quality prediction for DCT-based compression / R. Kozhemiakin, V. Lukin, B. Vozel// in 2017 14th International Conference The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM).  2017.  IEEE.  P. 225-228.

Wang, Z. A universal image quality index / Z. Wang, A. C. Bovik // IEEE signal processing letters.  2002.  Vol. 9, No. 3.  P. 81-84.

FSIM: A feature similarity index for image quality assessment / L. Zhang, L. Zhang, X. Mou, D. Zhang // IEEE transactions on Image Processing.  2011.  Vol. 20, No. 8.  P. 2378-2386.

Hore, A. Image quality metrics: PSNR vs. SSIM / A. Hore, D. Ziou // in 2010 20th international conference on pattern recognition.  IEEE.  2010.  P. 2366-2369.

J. Nilsson and T. Akenine-Möller, “Understanding ssim,”2020, https://arxiv.org/ abs/2006.13846.

Oh, H. Visually lossless JPEG 2000 for remote image browsing / H. Oh, A. Bilgin, M. Marcellin// Information .  2016.  Vol. 7, No. 3.  45 p.

Jamel, A. L. E. M. Efficiency Spiht in compression and quality of image / A. L. E. M. Jamel.  Journal of the College of Education for Women.  2011.  Vol. 22, No. 3.  P. 627-637.

Satellite image remote sensing for identifying aircraft using SPIHT and NSCT /S. Doss, S. Pal, D. Akila, S. Jeyalaksshmi, T. N. Jabeen, G. Suseendran // IEEE Signal processing magazine.  2020.  Vol. 7, No. 5.  P. 631-634.

Design of a high-performance system for secure image communication in the internet of things / E. Kougianos, S. P. Mohanty, G. Coelho, U. Albalawi, P. Sundaravadivel // IEEE Access.  2016.  Vol. 4.  P. 1222-1242.

Mentzer, F. Learning better lossless compression using lossy compression /F. Mentzer, L. V. Gool, M. Tschannen // in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).  2020.  Seattle, WA, USA,  P. 6638-6647.

USC-SIPI. The USC-SIPI image database Available: http://sipi.usc.edu/ database/database.php?volume=aerials

Athar, S. A comprehensive performance evaluation of image quality assessment algorithms /S. Athar, Z. Wang // IEEE Access.  2019.  Vol. 7.  P. 140030–140070.

Chandler, D. Seven challenges in image quality assessment: Past, present, and future research / D. Chandler.  ISRN Signal Process.  2013. 

p.

2D and 3D image quality assessment: A survey of metrics and challenges /Y. Niu, Y. Zhong, W. Guo, Y. Shi, P. Chen // IEEEAccess 2019.  Vol. 7.  P. 782–801.

Zhai, G. Perceptual image quality assessment: A survey / G. Zhai, X. Min // Sci. China Inf. Sci.  2020.  Vol. 63.  211301 p.

Okarma, K. Combined full-reference image quality metric linearly correlated with subjective assessment. In Artificial Intelligence and Soft Computing /L. Rutkowski, R. Scherer, R. Tadeusiewicz, L. Zadeh, J. Zurada, Eds. // Springer: Berlin/Heidelberg, Germany.  2010.  Vol. 6113.  P. 539–546.

Okarma, K. Combined image similarity index / K. Okarma.  OptRev 2012.  Vol. 19.  P. 349–354.

Liu, T.J. Image quality assessment using multi-method fusion /T.J. Liu, W. Lin, C.C.J. Kuo // IEEE Trans. Image Process.  2013. Vol.  22. 

P. 1793–1807.

Combining full-reference image visual quality metrics by neural network /V. Lukin, N. Ponomarenko, O. Ieremeiev, K. Egiazarian, J. Astola // In Human Vision and Electronic Imaging XX.  SPIE: Bellingham.  WA, USA.  2015.  93940K p.

Quality Assessment of 3D Printed Surfaces Using Combined Metrics Based on Mutual Structural Similarity Approach Correlated with Subjective Aesthetic Evaluation / K. Okarma, J. Fastowicz, P. Lech, V. Lukin // Appl. Sci.  2020.  Vol. 10.  6248 p.

Oszust, M. A Regression-Based Family of Measures for Full-Reference Image Quality Assessment / M. Oszust.  Meas. Sci. Rev.  2016.  Vol. 16.  P. 316–325.

Oszust, M. Decision Fusion for Image Quality Assessment using an Optimization Approach // M. Oszust.  IEEE Signal Process.  Lett. 2016.  Vol. 23.  P. 65–69.

Image database TID2013: Peculiarities, results and perspectives /N. Ponomarenko, L. Jin, O. Ieremeiev, V. Lukin, K. Egiazarian, J. Astola, B. Vozel, K. Chehdi, M. Carli, F. Battisti, et al. // Signal Process. Image Commun.  2015.  Vol. 30. P. 57–77.

Sun, W. MDID: A multiply distorted image database for image quality assessment / W. Sun, F. Zhou, Q. Liao // PatternRecognit.  2017.  Vol. 61.  P. 153–168.

Wang, Z. A universal image quality index / Z. Wang, A.C. Bovik // IEEE Signal Process. Lett.  2002.  Vol. 9.  P. 81–84.

Image quality assessment: From error visibility to structural similarity /Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli // IEEE Trans. Image Process.  2004.  Vol. 13.  P. 600–612.

Wang, Z. Multiscale structural similarity for image quality assessment /Z. Wang, E.P. Simoncelli, A.C. Bovik // In Proceedings of the 37th Asilomar Conference on Signals, Systems and Computers, Pacific Grove.  CA, USA.  9–12 November 2003.  P. 1398–1402.

Wang, Z. Information content weighting for perceptual image quality assessment /Z. Wang, Q. Li // IEEE Trans. Image Process.  2011. Vol.  20.  P. 1185–1198.

Complex Wavelet Structural Similarity: A New Image Similarity Index /M.P. Sampat, Z. Wang, S. Gupta, A.C. Bovik, M.K. Markey // IEEE Trans. Image Process.  2009.  Vol. 18.  P. 2385–2401.

FSIM: A feature similarity index for image quality assessment / L. Zhang, L. Zhang, X. Mou, D. Zhang // IEEE Trans. Image Process.  2011.  Vol. 20.  P. 2378–2386.

Image Quality Assessment based on Local Variance / S. Aja-Fernandez, R.S.J. Estepar, C. Alberola-Lopez, C.F. Westin // In Proceedings of the 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.  New York, NY, USA.  31 August–3 September 2006.  P. 4815–4818.

Structural Similarity index with predictability of image blocks /M. Ponomarenko, K. Egiazarian, V. Lukin, V. Abramova // In Proceedings of the 17th International Conference on Mathematical Methods in Electromagnetic Theory (MMET).  Kiev, Ukraine.  2–5 July 2018. 

P. 115–118.

Sheikh, H.R. Image information and visual quality /H.R. Sheikh, A.C. Bovik // IEEE Trans. Image Process.  2006.  Vol. 15.  P. 430–444.

Sheikh, H.R. An information fidelity criterion for image quality assessment using natural scene statistics / H.R. Sheikh, A.C. Bovik, G. Veciana // IEEE Trans. Image Process.  2005.  Vol. 14.  P. 2117–2128.

Image quality assessment based on DCT subband similarity / A. Balanov, A. Schwartz, Y. Moshe, N. Peleg// In Proceedings of the International Conference on Image Processing (ICIP).  Quebec City, QC, Canada.  27 September 2015.  P. 2105–2109.

Dumic, E. IQM2: New image quality measure based on steerable pyramid wavelet transform and structural similarity index /E. Dumic, S. Grgic, M. Grgic // SIViP 2014.  Vol. 8.  P. 1159–1168.

Perceptual quality metric with internal generative mechanism / J. Wu, W. Lin, G. Shi, A. Liu // IEEE Trans. Image Process.  2013.  Vol. 22. 

P. 43–54.

Perceptual image quality assessment by independent feature detector / H.W. Chang, Q.W. Zhang, Q.G. Wu, Y. Gan // Neurocomputing 2015.  Vol. 151.  P. 1142–1152.

A fast reliable image quality predictor by fusing micro- and macro-structures /K. Gu, L. Li, H. Lu, X. Min, W. Lin // IEEE Trans. Ind. Electron. 2017.  Vol. 64.  P. 3903–3912.

Sparse Feature Fidelity for perceptual image quality assessment /H.W. Chang, H. Yang, Y. Gan, M.H. Wang // IEEE Trans. Image Process. 2013.  Vol. 22.  P. 4007–4018.

Temel, D. UNIQUE: Unsupervised Image Quality Estimation. /D. Temel, M. Prabhushankar, G. AlRegib // IEEE Signal Process. Lett. 2016.  Vol. 23.  P. 1414–1418.

Prabhushankar, M. MS-UNIQUE: Multi-model and Sharpness-weighted Unsupervised Image Quality Estimation /M. Prabhushankar, D. Temel, G. AlRegib // Electron. Imaging.  2017.  P 30–35.

Neural network-based full-reference image quality assessment /S. Bosse, D. Maniry, K.R. Muller, T. Wiegand, W. Samek // In Proceedings of the 2016 Picture Coding Symposium (PCS).  Nuremberg, Germany.  4–7 December 2016.  P. 1–5.

Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment /S. Bosse, D. Maniry, K.R. Muller, T. Wiegand, W. Samek // IEEE Trans. Image Process. 201.  Vol. 27.  P. 206–219.

Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index /W. Xue, L. Zhang, X. Mou, A.C. Bovik // IEEE Trans. Image Process.  2014.  Vol. 23.  P. 684–695.

A Haar wavelet-based perceptual similarity index for image quality assessment /R. Reisenhofer, S. Bosse, G. Kutyniok, T. Wiegand// Signal Process. Image Commun. 2018.  Vol. 61.  P. 33–43.

RVSIM: A feature similarity method for full-reference image quality assessment /G. Yang, D. Li, F. Lu, Y. Liao, W. Yang // J. Image Video Proc. 2018.  2018.  P. 6.

Zhang, L. RFSIM: A feature based image quality assessment metric using Riesz transforms /L. Zhang, L. Zhang, X. Mou// In Proceedings of the 2010 IEEE International Conference on Image Processing.  Hong Kong, China.  26–29 September 2010.  P. 321–324.

Jia, H. Contrast and Visual Saliency Similarity-Induced index for assessing image quality /H. Jia, L. Zhang, T. Wang// IEEE Access 2018.  Vol. 6.  P. 65885–65893.

Cheraaqee, P. Incorporating gradient direction for assessing multiple distortions /P. Cheraaqee, A. Mansouri, A. Mahmoudi-Aznaveh// In Proceedings of the 4th International Conference on Pattern Recognition and Image Analysis (IPRIA).  Tehran, Iran.  6–7 March 2019.  P. 109–113.

Quality assessment of images with multiple distortions based on phase congruency and gradient magnitude /X. Miao, H. Chu, H. Liu, Y. Yang, X. Li // Signal Process. Image Commun. 2019.  Vol. 79.  P. 54–62.

Mitsa, T. Evaluation of contrast sensitivity functions for the formulation of quality measures incorporated in halftoning algorithms /T. Mitsa, K. Varkur// In Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing.  Minneapolis, MN, USA.  27–30 April 1993.  Vol. 5.  P. 301–304.

Modified image visual quality metrics for contrast change and mean shift accounting /N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, M. Carli// In Proceedings of the 2011 11th International Conference The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM).  Polyana, Ukraine.  23–25 February 2011.  P. 305–311.

Chandler, D. VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images /D. Chandler, S. Hemami// IEEE Trans. Image Process.  2007.  Vol. 16.  P. 2284–2298.

Zhang, L. VSI: A Visual Saliency-Induced Index for Perceptual Image Quality Assessment /L. Zhang, Y. Shen, H. Li// IEEE Trans. Image Process.  2014.  Vol. 23.  P. 4270–4281.

Multiscale contrast similarity deviation: An effective and efficient index for perceptual image quality assessment /T. Wang, L. Zhang, H. Jia, B. Li, H. Shu// Signal Process. Image Commun.  2016.  Vol. 45.  P. 1–9.

Zhang, L. SR-SIM: A fast and high performance IQA index based on spectral residual / L. Zhang, H. Li // In Proceedings of the 2012 19th IEEE International Conference on Image Processing.  Orlando, FL, USA.  30 September–3 October 2012.  P. 1473–1476.

Wavelet Based Sharp Features (WASH): An Image Quality Assessment Metric Based on HVS /M. Reenu, D. David, S.S.A. Raj, M.S. Nair// In Proceedings of the 2013 2nd International Conference on Advanced Computing, Networking and Security.  Mangalore, India.  15–17 December 2013.  P. 79–83.

Toward Accurate Quality Estimation of Screen Content Pictures With Very Sparse Reference Information /Z. Xia, K. Gu, S. Wang, H. Liu, S. Kwong// IEEE Trans. Ind. Electron.  2020.  Vol. 67.  P. 2251–2261.

Gradient Direction for Screen Content Image Quality Assessment /Z. Ni, L. Ma, H. Zeng, C. Cai, K.K. Ma// IEEE Signal Process. Lett.  2016.  Vol. 23.  P. 1394–1398.

The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement /K. Gu, G. Zhai, W. Lin, M. Liu// IEEE Trans. Cybern.  2016.  Vol. 46.  P. 284–297.

Objective quality assessment of multiply distorted images /D. Jayaraman, A. Mittal, A.K. Moorthy, A.C. Bovik// In Proceedings of the 46th Asilomar Conference on Signals, Systems and Computers (ASILOMAR).  Pacific Grove, CA, USA.  4–7 November 2012.

Hybrid no-reference quality metric for singly and multiply distorted images / K. Gu, G. Zhai, X. Yang, W. Zhang // IEEE Trans. Broadcast.  2014.  Vol. 60.  P. 555–567.

Corchs, S. A multidistortion database for image quality / S. Corchs, F. Gasparini // In Computational Color Imaging. CCIW 2017.  Springer: Berlin/Heidelberg, Germany.  2017.  Vol. 10213.  P. 95–104.

Ghadiyaram, D. Massive Online Crowdsourced Study of Subjective and Objective Picture Quality / D. Ghadiyaram, A.C. Bovik // IEEE Trans. Image Process.  2016.  Vol. 25.  P. 372–387.

Опубліковано

2023-06-30

Як цитувати

Лі, Ф., Лукін, В., Абрамов, С., Рубель, А., Окарма, К., Лєх, П., Хлевицьки, В., & Копитек, М. (2023). ЕФЕКТИВНІ МЕТОДИ ІНТЕЛЕКТУАЛЬНОЇ ОБРОБКИ ЗОБРАЖЕНЬ ТА ВІДЕО НА ОСНОВІ МЕТРИК ВІЗУАЛЬНОЇ ЯКОСТІ ДЛЯ ПЕРСПЕКТИВНИХ ЗАСТОСУВАНЬ. European Science, 1(sge21-01), 94–145. https://doi.org/10.30890/2709-2313.2023-21-01-012

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