Near lossless image compression using parallel fractal texture identification 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 Floodfill 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.

Lossless and nearlossless image compression with successive refinement We present a technique that provides progressive transmission and nearlossless compression in one single framework. The proposed technique produces a bitstream that results in progressive reconstruction of the image justlike what one can obtain with a reversible wavelet codec. In addition, the proposed scheme provides nearlossless reconstruction with respect to a given bound after each layer of the successively refinable bitstream is decoded. We formulate the image data compression problem as one of asking the optimal questions to determine, respectively, the value or the interval of the pixel, depending on whether one is interested in lossless or nearlossless compression. New prediction methods based on the nature of the data at a given pass are presented and links to the existing methods are explored. The tradeoff between noncausal prediction and data precision is discussed within the context of successive refinement. Context selection for prediction in different passes is addressed. Finally, experimental results for both lossless and nearlossless cases are presented, which are competitive with the stateoftheart compression schemes.