ЕФЕКТИВНІ МЕТОДИ ІНТЕЛЕКТУАЛЬНОЇ ОБРОБКИ ЗОБРАЖЕНЬ ТА ВІДЕО НА ОСНОВІ МЕТРИК ВІЗУАЛЬНОЇ ЯКОСТІ ДЛЯ ПЕРСПЕКТИВНИХ ЗАСТОСУВАНЬ
DOI:
https://doi.org/10.30890/2709-2313.2023-21-01-012Ключові слова:
Visual quality, combined metrics, remote sensing, multiple distortionsАнотація
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 beenMetrics
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.
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