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
Abir Chakraborty
Abir Chakraborty, Department of Computer Science, Project Work Team Fellow, University of Coimbra, Kolkata (West Bengal), India.
Manuscript Received on 01 November 2024 | First Revised Manuscript Received on 02 December 2024 | Second Revised Manuscript Received on 25 February 2025 | Manuscript Accepted on 15 March 2025 | Manuscript published on 30 March 2025 | PP: 23-26 | Volume-15 Issue-1, March 2025 | Retrieval Number: 100.1/ijsce.F365814060125 | DOI: 10.35940/ijsce.F3658.15010325
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Rgb, Lab, Gray, Prewitt, Sobel, Canny Filtering, K-Means Clustering Method.
Scope of the Article: Image Processing and Recognition