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General scheme of lossless predictive image compression.

General scheme of lossless predictive image compression.

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Among the many categories of images that require lossless compression, medical images can be indicated as one of the most important category. Medical image compression with loss impairs of diagnostic value, therefore, there are often legal restrictions on the image compression with losses. Among the common approaches to medical image compression we...

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... compression is based on several stages, as shown in Fig. 1, that are: prediction, contextual modeling, error modeling and entropy coding. This type of lossless method of compression became a standard during the last decade of the last century. At the time, several algorithms were proposed and tested for the purpose of adoption of a standard for lossless image compression [2]. Prediction is the ...

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... Prediction is a crucial part of compression because it can remove most of the spatial redundancy between pixels, and the choice of an optimal predictor is essential for the efficiency of compression methods. Linear predictors have a significant advantage which is the possibility of realization of the integer system [3]. In our method, a proposed twodimensional linear prediction method is used to remove the inter-pixel redundancy of images. ...
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... MED and GAP were analyzed and comparative analysis of these predictors were also done in terms of entropy. Authors in [15] adopted a compression method that is based on a combination between predictive coding and bit plane slicing for compression of medical and natural image samples. High system performance is achieved by this lossless compression technique with high compression ratio. ...
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... Avramovic and Savic proposed a predictive algorithm for the estimation of local gradients and detection of edges. Entropy analysis for different predictors is done after prediction for different images like CT and MRI [15]. Owen Zhao et al. proposed an efficient lossless image compression scheme called super-spatial structure prediction [16]. ...
... A combination of both standard predictors results in GED that takes the merit of simplicity and efficiency from both MED and GAP predictors. GED is also threshold based, just like GAP, but the threshold value is user-defined in case of GED [15]. In literature, different encoding techniques are available like Huffman, run-length, Dictionary, arithmetic and bit-plane coding, etc. ...
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... Prediction is the important part of the compression, because it removes most of the spatial redundancy, and the choice of the optimal predictor is essential for the efficiency of compression methods. The prediction may be linear or nonlinear [12]. Linear predictors based on the finite group of sub predictors, are simple and fast. ...
... Linear predictors based on the finite group of sub predictors, are simple and fast. Nonlinear prediction is based on neural networks, vector quantization, etc [12]. Contextual modeling means adaptive correction of prediction of pixels in order to exploit repeated schemes in a picture. ...
... Error modeling can further reduce the entropy of prediction error image. Entropy coding removes statistical redundancy of the prediction error images [12]. ...
... Ferni Ukrit et al. (2011) have performed a survey on various lossless compressing techniques. Avramovic and Savić (2011) have described predictive lossless image compression process. Sridevi et al. (2012) have used various medical image compression techniques such as JPEG2000 image compression, JPEG2000 scaling-based ROI coding. ...
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... Prediction is the important part of the compression, because it removes most of the spatial redundancy, and the choice of the optimal predictor is essential for the efficiency of compression methods. The prediction may be linear or nonlinear [12]. Linear predictors based on the finite group of sub predictors, are simple and fast. ...
... Linear predictors based on the finite group of sub predictors, are simple and fast. Nonlinear prediction is based on neural networks, vector quantization, etc [12]. Contextual modeling means adaptive correction of prediction of pixels in order to exploit repeated schemes in a picture. ...
... Error modeling can further reduce the entropy of prediction error image. Entropy coding removes statistical redundancy of the prediction error images [12]. ...
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... h v bin cont W N NW WW NN Er g g Er P    (7) where first five inputs of cont function are neighbor pixels, Er is a prediction error for previous pixels, g h and g v are local gradient estimations and P is current pixels prediction. First five bits of unique binary number are determined by comparing of neighbor pixels with current prediction. ...
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In this paper, a novel predictive-based lossless image compression algorithm is presented. Lossless compression must be applied when data acquisition is important and expensive, as in aerial, medical and space imaging. Besides requirements of high compression ratios as much as it is possible, lossless image coding algorithms must be fast. Proposed algorithm is developed for efficient and fast processing of 12-bit medical images. Comparison with standardized lossless compression algorithm, JPEG-LS is done on a set of 12-bit medical images with different statistical features. It is shown that proposed solution can achieve approximately same bitrates as JPEG-LS even though it is much simpler.