Evaluation of Classification Accuracy with Original and Compressed Images
Vanitha Kakollu1, Chandrasekhar Reddy P2
1Vanitha kakollu, Department of Computer Science, GIS, GITAM (Deemed to be University), Visakhapatnam (A.P), India.
2Chandrasekhar Reddy P, Department of Computer Science, GIS, GITAM (Deemed to be University), Visakhapatnam (A.P), India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 162-166 | Volume-8 Issue-4, November 2019. | Retrieval Number: C6584098319/2019©BEIESP | DOI: 10.35940/ijrte.C6584.118419
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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: The extent of communicated information through internet has augmented speedily over the past few years. Image compression is the preeminent way to lessen the size of the image. JPEG is the one the best technique related to lossy image compression. In this paper a novel JPEG compression algorithm with Fuzzy-Morphology techniques was proposed. The efficacy of the proposed algorithm compared to JPEG is presented with metrics like PSNR, MSE, No of bits transmitted. The proposed approaches lessen the number of encoded bits as a result tumbling the quantity of memory needed. The Planned approaches are best appropriate for the images corrupted with Gaussian, Speckle, Poisson, Salt & Pepper noises. In this paper the effect of compression on classification performance was envisaged, Artificial Neural Network, Support Vector Machine, and, KNN classifiers performance is evaluated with original image data, standard JPEG compressed data and the compressed image data with the proposed method.
Keywords: JPEG, Fuzzy Morphology, PNSR, MSE, KNN..
Scope of the Article: Fuzzy Logics.