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
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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.
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“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. DOI: http://dx.doi.org/10.54105/ijipr.D1029.04030424
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]. https://www.researchgate.net/publication/364059911
Detectionofsignal Tonoise Ratio From Image Contour -A Matlab Application [Volume: 06 Issue: 09 | September – 2022, ISSN: 2582-3930]. https://github.com/MaorAssayag/Digital-Image-Processing/blob/master/README.md
Application Of Image Processing Using Matlab- A Practical Handbook For Image Processing Laboratorty]-Abir Chakraborty. https://www.amazon.in/APPLICATIONS-PROCESSING-PRACTICAL-HANDBOOK-LABORATORY/dp/8196425074
Ahmed, S. & Alone, M. R. (2014). Image Compression using Neural Network. International Journal of Innovative Science and Modern Engineering, 2(5), 24-28. https://www.academia.edu/7518165/Image_Compression_using_Neural_Network
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. DOI: 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. DOI: 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. DOI: 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. DOI: 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. DOI: https://doi.org/10.17485/ijst/2015/v8i15/73855
Tamanna, & Bassan, N. (2019). Innovative Hybridization for Image Compression using PCA and Multilevel 2D-Wavelet. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 3, pp. 2411–2415). DOI: https://doi.org/10.35940/ijrte.c4668.098319
Sankaran, B. G., Karthik, B., & Vijayaragavan, S. P. (2019). Weight Ward Change Region Plummeting Change for Square based Image Huffman Coding. In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 10, pp. 4313–4316). DOI: https://doi.org/10.35940/ijitee.j9841.0881019
Saha, T., & Vishal, Dr. K. (2024). A Study of Application of Digital Image Processing in Medical Field and Medical Image Segmentation by Edge Detection. In International Journal of Emerging Science and Engineering (Vol. 12, Issue 4, pp. 3–8). DOI: https://doi.org/10.35940/ijese.g9890.12040324
Shobana, G., Suguna, Dr. M., & Umamageshwari, C. (2019). Smart Adoption System using Image Processing Techniques. In International Journal of Engineering and Advanced Technology (Vol. 8, Issue 6s, pp. 84–87). DOI: https://doi.org/10.35940/ijeat.f1017.0886s19
A., O., & O, B. (2020). An Iris Recognition and Detection System Implementation. In International Journal of Inventive Engineering and Sciences (Vol. 5, Issue 8, pp. 8–10). DOI: https://doi.org/10.35940/ijies.h0958.025820