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2 2013

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Discrete Tomography Data Footprint Reduction via Natural Compression

Fiorini R. A., Condorelli A., Laguteta G.

Fundamenta Informaticae

2013

Vol. 125, nr 3/4

273--284

In Discrete Tomography (DT) by electron microscopy, 2-D projection images are acquired from various angles, by tilting the sample, generating new challenges associated with the problem of formation, acquisition, compression, transmission, and analysis of enormous quantity of data. Data Footprint Reduction (DFR) is the process of employing one or more techniques to store a given set of data in less storage space. Modern lossless compressors use classical probabilistic models only, and are unable to match high end application requirements like “Arbitrary Bit Depth” (ABD) resolution and information “Dynamic Upscale Regeneration” (DUR). Traditional \mathbbQ Arithmetic can be regarded as a highly sophisticated open logic, powerful and flexible bidirectional (LTR and RTL) formal language of languages, according to brand new “Information Conservation Theory” (ICT). This new awareness can offer competitive approach to guide more convenient algorithm development and application for combinatorial lossless compression, we named “Natural Compression” (NC). To check practical implementation performance, a first raw example is presented, benchmarked to standard, more sophisticate lossless JPEG2000 algorithm, and critically discussed. NC raw overall lossless compression performance compare quite well to standard one, but offering true ABD and DUR at no extra computational cost.

Discrete Tomography Data Footprint Reduction by Information Conservation

Fiorini R. A., Laguteta G.

Fundamenta Informaticae

2013

Vol. 125, nr 3/4

261--272

The first impact of Discrete Tomography (DT) applied to nanoscale technology has been to generate enormous quantity of data. Data Footprint Reduction (DFR) is the process of employing one or more techniques to store a given set of data in less storage space. The very best modern lossless compressors use classical probabilistic models only, and are unable to match high end application requirements, like “Arbitrary Bit Depth” (ABD) resolution and “Dynamic Upscale Regeneration” (DUR), with full information conservation. This paper explores, at core level, the basic properties and relationships of Q Arithmetic to achieve full numeric information conservation and regeneration, algorithmically. That knowledge shows strong connections to modular group theory and combinatorial optimization. Traditional Q Arithmetic can be even regarded as a highly sophisticated open logic, powerful and flexible LTR and RTL formal numeric language of languages, with self-defining consistent word and rule, starting from elementary generator and relation. This new awareness can guide the development of successful more convenient algorithm and application.