Abstract
This paper presents a comparative performance evaluation of three different image compression techniques for content type in World Wide Web. One is based on fractal compression technique and the other two are JPEG and Portable Network Graphics (PNG). It is due to the unavailability of fractal-based compression as an image type, a pluggable system to prove its feasibility and superior performance has been developed. Study includes the development of a test system to convert and upload the raw image as fractal compressed image to the major content delivery networks. A mobile application for downloading the rendered image is also developed for the client side system. The present system utilizes a Reinforcement Learning (RL) algorithm to reduce the encoding time required to compress an image by using classical Iterative Function System (IFS). The algorithm also employs modified Horizontal-Vertical (HV) partitioning scheme and upper bounded scaling, translation and shifting parameters. The empirical analysis proves that the usage of fractal compressed images can be a promising method for reducing the network traffic, and hence transmission bandwidth of content delivery networks.
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Varghese, B., Krishnakumar, S. (2021). A Pluggable System to Enable Fractal Compression as the Primary Content Type for World Wide Web. In: Bajpai, M.K., Kumar Singh, K., Giakos, G. (eds) Machine Vision and Augmented Intelligence—Theory and Applications. Lecture Notes in Electrical Engineering, vol 796. Springer, Singapore. https://doi.org/10.1007/978-981-16-5078-9_55
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DOI: https://doi.org/10.1007/978-981-16-5078-9_55
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