Powiadomienia systemowe
- Sesja wygasła!
- Sesja wygasła!
Tytuł artykułu
Treść / Zawartość
Pełne teksty:
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Image quality assessment methods are used in different image processing applications. Among them, image compression and image super-resolution can be mentioned in wireless capsule endoscopy (WCE) applications. The existing image compression algorithms for WCE employ the generalpurpose image quality assessment (IQA) methods to evaluate the quality of the compressed image. Due to the specific nature of the images captured by WCE, the general-purpose IQA methods are not optimal and give less correlated results to that of subjective IQA (visual perception). This paper presents improved image quality assessment techniques for wireless capsule endoscopy applications. The proposed objective IQA methods are obtained by modifying the existing full-reference image quality assessment techniques. The modification is done by excluding the noninformative regions, in endoscopic images, in the computation of IQA metrics. The experimental results demonstrate that the proposed IQA method gives an improved peak signal-tonoise ratio (PSNR) and structural similarity index (SSIM). The proposed image quality assessment methods are more reliable for compressed endoscopic capsule images.
Wydawca
Rocznik
Tom
Strony
18--24
Opis fizyczny
Bibliogr. 30 poz., rys., tab.
Twórcy
autor
- Faculty of Electrical and Computer Engineering, Jimma Institute of Technology, Jimma University Ethiopia
autor
- Faculty of Electrical and Computer Engineering, Jimma Institute of Technology, Jimma University Ethiopia
autor
- Faculty of Electrical and Computer Engineering, Jimma Institute of Technology, Jimma University Ethiopia
Bibliografia
- [1] G. Iddan, G. Meron, A. Glukhovsky, and P. Swain, “Wireless capsule endoscopy,” Nature, vol. 405, no. 6785, pp. 417-417, 2000.
- [2] K.A. Fante, B. Bhaumik, and S. Chatterjee, “Design and implementation of computationally efficient image compressor for wireless capsule endoscopy,” Circuits, Systems, and Signal Processing, vol. 35, no. 5, pp. 1677-1703, 2016.
- [3] C. Cheng, Z. Liu, C. Hu, and M.Q.-H. Meng, “A novel wireless capsule endoscope with jpeg compression engine,” in 2010 IEEE International Conference on Automation and Logistics. IEEE, 2010, pp. 553-558.
- [4] Y. Wang, C. Cai, and Y. Zou, “Single image super-resolution via adaptive dictionary pair learning for wireless capsule endoscopy image,” in 2015 IEEE International Conference on Digital Signal Processing (DSP). IEEE, 2015, pp. 595-599.
- [5] P.A. Floor, I. Farup, M. Pedersen, and Ø. Hovde, “Error reduction through post processing for wireless capsule endoscope video,” EURASIP Journal on Image and Video Processing, vol. 2020, pp. 1-15, 2020.
- [6] S. Charfi and M. El Ansari, “Computer-aided diagnosis system for colon abnormalities detection in wireless capsule endoscopy images,” Multimedia Tools and Applications, vol. 77, no. 3, pp. 4047-4064, 2018.
- [7] R. Istepanian, N. Philip, M. Martini, N. Amso, and P. Shorvon, “Subjective and objective quality assessment in wireless teleultrasonography imaging,” in 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008, pp. 5346-5349.
- [8] Z. Wang and A.C. Bovik, “Modern image quality assessment,” Synthesis Lectures on Image, Video, and Multimedia Processing, vol. 2, no. 1, pp. 1-156, 2006.
- [9] J. Korhonen and J. You, “Peak signal-to-noise ratio revisited: Is simple beautiful?” in 2012 Fourth International Workshop on Quality of Multimedia Experience. IEEE, 2012, pp. 37-38.
- [10] Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing, vol. 13, no. 4, pp. 600-612, 2004.
- [11] L. Zhang, L. Zhang, X. Mou, and D. Zhang, “Fsim: A feature similarity index for image quality assessment,” IEEE transactions on Image Processing, vol. 20, no. 8, pp. 2378-2386, 2011.
- [12] M.-C. Lin, L.-R. Dung, and P.-K. Weng, “An ultra-low-power image compressor for capsule endoscope,” BioMedical Engineering OnLine, vol. 5, no. 1, pp. 1-8, 2006.
- [13] P.C. Cosman, R.M. Gray, and R.A. Olshen, “Evaluating quality of compressed medical images: Snr, subjective rating, and diagnostic accuracy,” Proceedings of the IEEE, vol. 82, no. 6, pp. 919-932, 1994.
- [14] L. He, F. Gao, W. Hou, and L. Hao, “Objective image quality assessment: a survey,” International Journal of Computer Mathematics, vol. 91, no. 11, pp. 2374-2388, 2014.
- [15] P. Turcza and M. Duplaga, “Low power fpga-based image processing core for wireless capsule endoscopy,” Sensors and Actuators A: Physical, vol. 172, no. 2, pp. 552-560, 2011.
- [16] K.A. Fante, B. Bhaumik, and S. Chatterjee, “A low-power color mosaic image compressor based on optimal combination of 1-d discrete wavelet packet transform and dpcm for wireless capsule endoscopy.” in BIODEVICES, 2015, pp. 190-197.
- [17] I. Intzes, H. Meng, and J. Cosmas, “An ingenious design of a high performance-low complexity image compressor for wireless capsule endoscopy,” Sensors, vol. 20, no. 6, p. 1617, 2020.
- [18] C. Babu, D.A. Chandy, and P. Karthigaikumar, “Novel chroma subsampling patterns for wireless capsule endoscopy compression,” Neural Computing and Applications, vol. 32, no. 10, pp. 6353-6362, 2020.
- [19] B. Sushma and P. Aparna, “Distributed video coding based on classification of frequency bands with block texture conditioned key frame encoder for wireless capsule endoscopy,” Biomedical Signal Processing and Control, vol. 60, p. 101940, 2020.
- [20] M. A. Usman, M. R. Usman, and S. Y. Shin, “Quality assessment for wireless capsule endoscopy videos compressed via hevc: From diagnostic quality to visual perception,” Computers in biology and medicine, vol. 91, pp. 112–134, 2017.
- [21] M. A. Usman and M. G. Martini, “On the suitability of vmaf for quality assessment of medical videos: Medical ultrasound & wireless capsule endoscopy,” Computers in biology and medicine, vol. 113, p. 103383, 2019.
- [22] L. L´eveque, H. Liu, S. Barakovic, J.B. Husic, M. Martini, M. Outtas, L. Zhang, A. Kumcu, L. Platisa, R. Rodrigues et al., “On the subjective assessment of the perceived quality of medical images and videos,” in 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2018, pp. 1-6.
- [23] M.-C. Lin and L.-R. Dung, “A subsample-based low-power image compressor for capsule gastrointestinal endoscopy,” EURASIP Journal on Advances in Signal Processing, vol. 2011, no. 1, p. 257095, 2011.
- [24] “Gastrolab,” date last accessed January 10,2020. [Online]. Available: http://gastrolab.net/.
- [25] Q. Huynh-Thu and M. Ghanbari, “The accuracy of psnr in predicting video quality for different video scenes and frame rates,” Telecommunication Systems, vol. 49, no. 1, pp. 35-48, 2012.
- [26] A. Hore and D. Ziou, “Image quality metrics: Psnr vs. ssim,” in 2010 20th international conference on pattern recognition. IEEE, 2010, pp. 2366-2369.
- [27] Z. Kotevski and P. Mitrevski, “Experimental comparison of psnr and ssim metrics for video quality estimation,” in International Conference on ICT Innovations. Springer, 2009, pp. 357-366.
- [28] L.-R. Dung, Y.-Y. Wu, H.-C. Lai, and P.-K. Weng, “A modified h. 264 intra-frame video encoder for capsule endoscope,” in 2008 IEEE Biomedical Circuits and Systems Conference. IEEE, 2008, pp. 61-64.
- [29] P. Turcza and M. Duplaga, “Hardware-efficient low-power image processing system for wireless capsule endoscopy,” IEEE journal of biomedical and health informatics, vol. 17, no. 6, pp. 1046-1056, 2013.
- [30] K. Wahid, S.-B. Ko, and D. Teng, “Efficient hardware implementation of an image compressor for wireless capsule endoscopy applications,” in 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). IEEE, 2008, pp. 2761-2765.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3fcb8f9c-4408-4e31-b014-d6247827a1f9