Published October 30, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

A Comparative Study of Mean Square Error, Dimensions, Signal to Noise Ratio of Colored and Non-Colored Clustered Original Images Along with Compressed Version After the Image Segmentation and Filtering Method

Creators

  • 1. Department of Computer Science, Project Work Team Fellow, University of Coimbra, Kolkata (West Bengal), India.

Description

Abstract: Primarily author has already done one fundamental paper work on image clustering and segmentation but here in this paper author has continued that same type of work on clustered and segmented images as a mode of comparative study for author has chosen three different parameters like mean square error, peak SNR and dimensions of images (length, width, height). The author has all three parametric methods on one particular to justify the comparison. So this paper is a cumulative case of a comparative study for which author has chosen the above mentioned parameters to justify the best results of the clustered and segmented images.

Files

F103204061024.pdf

Files (327.5 kB)

Name Size Download all
md5:5faa8df9d269c9a764c8523d22050630
327.5 kB Preview Download

Additional details

Identifiers

DOI
10.54105/ijipr.F1032.04061024
EISSN
2582-8037

Dates

Accepted
2024-10-15
Manuscript received on 12 September 2024 | Revised Manuscript received on 07 October 2024 | Manuscript Accepted on 15 October 2024 | Manuscript published on 30 October 2024.

References

  • "Comparison Of Signal To Noise Ratio Of Colored And Gray Scale Image In Clustered Condition From The Contour Of The Images With The Help Of Different Image Filtering Method"- Abir Chakraborty, Volume 9, Issue 5 May 2024| ISSN: 2456-4184
  • Detection and Comparison of Signal To Noise Ratio's and Other Dimensions Related Specifications From Contours of Several Images - A Matlab Syntax Based Applications of Biomedical and General Jpeg Images- Abir Chakraborty, Dr. Somshekhar Bhat, Dr. Kumar Shama [Volume 10, Issue 9, September-2022, Impact Factor: 7.429, ISSN: 2455-6211]
  • Detectionofsignal Tonoise Ratio From Image Contour -A Matlab Application [Volume: 06 Issue: 09 | September – 2022, Issn: 2582-3930]
  • Application Of Image Processing Using Matlab- A Practical Handbook For Image Processing Laboratorty]-Abir Chakraborty
  • Detection and Comparison of Signal To Noise Ratio's and Other Dimensions Related Specifications From Contours of Several Images - A Matlab Syntax Based Applications of Biomedical and General Jpeg Images-[ Abir Chakraborty1, Dr. Somshekhar Bhat2, Dr. Kumar Shama3, 1,2,3Manipal Institute of Technology, Mahe , Karanataka, India, Volume 10, Issue 9, September-2022, Impact Factor: 7.429, ISSN: 2455-6211]
  • Ahmed, S. & Alone, M. R. (2014). Image Compression using Neural Network. International Journal of Innovative Science and Modern Engineering, 2(5), 24-28.
  • Balasubramani, P., & Murugan, P. R. (2015). Efficient image compression techniques for compressing multimodal medical images using neural network radial basis function approach. International Journal of Imaging Systems and Technology, 25(2), 115-122. https://doi.org/10.1002/ima.22127
  • Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4), 193-202. https://doi.org/10.1007/Bf00344251
  • Grgic, S., Grgic, M., & Zovko-Cihlar, B. (2001). Performance analysis of image compression using wavelets. IEEE Transactions on Industrial Electronics, 48(3), 682-695. https://doi.org/10.1109/41.925596
  • Hussain, A. J., Al-Jumeily, D., Radi, N., & Lisboa, P. (2015). Hybrid neural network predictive-wavelet image compression system. Neurocomputing, 151, 975-984. https://doi.org/10.1016/j.neucom.2014.02.078.
  • Joe, A. R., & Rama, N. (2015). Neural network based image compression for memory consumption in cloud environment. Indian Journal of Science and Technology, 8(15), 1-6. https://doi.org/10.17485/i jst/2015/ v8i15/73855,
  • Baig, M. A. (2021). An Efficient Cluster Based Routing Protocol (ECCRP) Technique Based on Weighted Clustering Algorithm for Different Topologies in Manets using Network Coding. In Indian Journal of Data Communication and Networking (Vol. 1, Issue 2, pp. 31–34). https://doi.org/10.54105/ijdcn.b5011.041221
  • Shaik, I., Nittela, S. S., Hiwarkar, Dr. T., & Nalla, Dr. S. (2019). K-means Clustering Algorithm Based on E-Commerce Big Data. In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 11, pp. 1910–1914). https://doi.org/10.35940/ijitee.k2121.0981119
  • Patravali, S. D., & Algur, Dr. S. P. (2023). COVID-19 Sentiment Analysis using K-Means and DBSCAN. In International Journal of Emerging Science and Engineering (Vol. 11, Issue 12, pp. 12–17). https://doi.org/10.35940/ijese.l2558.11111223
  • Maheswari, Dr. K. (2019). Finding Best Possible Number of Clusters using K-Means Algorithm. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1s4, pp. 533–538). https://doi.org/10.35940/ijeat.a1119.1291s419
  • Verma, Dr. P. K., & Dr. Preety. (2020). Application of K-Means Algorithm to Mapping Poverty Outline by Province in India. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 6, pp. 1045–1049). https://doi.org/10.35940/ijrte.f7357.038620