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• W3002049691 startingPage "101862" @default.
• W3002049691 abstract "The most important parameters of image processing are resolution of the image and processing speed. The datasets of multimedia are compressed, which are rich in quality and quantity is challenging. This paper develops a novel approach to estimate the affine parameters of fractal texture identification, in order to minimize the complexity of computation. In this proposed NLICPFTI, a pattern dictionary is maintained to hold repeated fractal patterns. Different types of data chunks such as Fractal Pattern Chunk (FPC), Intermediate Raster Chunk (IRC), Intermediate Vector Chunk (IVC), Run Length Encode Chunk (RLEC) and Flood-fill Zone Chunk (FFZC). These chunks are used to store an image into compressed format. Experimental on standard images illustrate that our approach gives more significant improvements in Peak Signal to Noise Ratio, Structural Similarity Index Mode, Feature Similarity Index Mode at high compression ratio." @default.
• W3002049691 created "2020-01-30" @default.
• W3002049691 creator A5038541733 @default.
• W3002049691 creator A5071053516 @default.
• W3002049691 date "2020-04-01" @default.
• W3002049691 modified "2023-10-01" @default.
• W3002049691 title "Near lossless image compression using parallel fractal texture identification" @default.
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• W3002049691 doi "https://doi.org/10.1016/j.bspc.2020.101862" @default.
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