An Efficient Approach to Lossy Compression with Pointwise
Relative Error Bound
Author/Presenter
Event Type
Workshop
TimeFriday, November 17th9:10am -
9:30am
Location302-303
DescriptionAn effective data compressor is becoming increasingly
critical because of the extremely large volumes of data
produced by scientific simulations. Many lossy
compressors have been developed in the context of
absolute error bounds (e.g., minimizing the maximum
absolute error or root-mean-squared error). In order to
achieve a multiresolution effect, however, scientific
applications may prefer to compress the data with a
pointwise relative error bound (i.e., the larger the
data value, the larger the compression error to
tolerate). In this paper, we explore a lossy compression
strategy based on the requirement of pointwise relative
error bound, under a state-of-the-art compression
framework (prediction + quantization + entropy
encoding). Specifically, we split the data set into many
small blocks and compute a real absolute error bound for
each block based on the pointwise relative error bound
set by users. We implement this solution and evaluate it
using a real-world scientific data set. Experiments show
that the compression ratio of our solution is higher
than that of other state-of-the-art compressors by
17.2–618%, with comparable compression/decompression
times, the same relative error bound, and similar peak
signal-to-noise ratio (PSNR).
Author/Presenter
