Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Large scale visualization on the Powerwall.
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

Events on October 17, 2018

Visualization Seminar

Duong Hoang Presents:

A Study of the Trade-off Between Reducing Precision and Reducing Resolution for Data Analysis and Visualization

October 17, 2018 at 12:00pm for 1hr
Evans Conference Room, WEB 3780
Warnock Engineering Building, 3rd floor.

Abstract:

duong compression
Visualization of the diffusivity field at 0.2 bits per sample (bps) and its Laplacian field at 1.5 bps, using two of the bit streams
studied in the paper. Compared to the by bit plane stream, the by wavelet norm stream produces a better reconstruction of the original
function (left, compare white features), and a slightly worse, if not comparable, reconstruction of the Laplacian field (right).


There currently exist two dominant strategies to reduce data sizes in analysis and visualization: reducing the precision of the data, e.g., through quantization, or reducing its resolution, e.g., by subsampling. Both have advantages and disadvantages and both face fundamental limits at which the reduced information ceases to be useful. The paper explores the additional gains that could be achieved by combining both strategies. In particular, we present a common framework that allows us to study the trade-off in reducing precision and/or resolution in a principled manner. We represent data reduction schemes as progressive streams of bits and study how various bit orderings such as by resolution, by precision, etc., impact the resulting approximation error across a variety of data sets as well as analysis tasks. Furthermore, we compute streams that are optimized for different tasks to serve as lower bounds on the achievable error. Scientific data management systems can use the results presented in this paper as guidance on how to store and stream data to make efficient use of the limited storage and bandwidth in practice.

Posted by: Steve Petruzza