Analytical and Empirical Survival Study on Natural Image Compression and Classification using Machine Learning Techniques
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© 2022 by IJCTT Journal
Volume-70 Issue-8
Year of Publication : 2022
Authors : M. Sakthivadivu, P. Suresh Babu
DOI :  10.14445/22312803/IJCTT-V70I8P104

How to Cite?

M. Sakthivadivu, P. Suresh Babu, "Analytical and Empirical Survival Study on Natural Image Compression and Classification using Machine Learning Techniques," International Journal of Computer Trends and Technology, vol. 70, no. 8, pp. 21-29, 2022. Crossref, https://doi.org/10.14445/22312803/IJCTT-V70I8P104

Abstract
Image processing is used to analyse and manipulate digitised images to increase image quality. Image pre-processing minimises noise, enhances contrast, smoothing and sharpening, and performs advanced operations. Feature extraction is the method of describing the set of features or image characteristics for analysis and classification. A feature is a piece of information about image content with properties. The feature extraction process extracts the essential features from the input image. Image classification is a process based on the segregation of object similarity values. Image compression is a method where the original image gets encoded with a small number of bits. Image compression is used for digital images to minimise storage and transmission costs. Many researchers carried out their research on natural image feature extraction, classification and compression methods. But, the peak signal-to-noise ratio was not improved, and time consumption was not reduced. The different image filtering, compression and classification methods with natural images are reviewed in analytical and empirical terms to address the existing problems.

Keywords
Contrast enhancement, Feature extraction, Filtering, Image classification, Image compression, Image smoothing, Image processing.

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