A Versatile Compression Method for Floating-Point Data Stream

2013 ◽  
Author(s):  
Songbin Liu ◽  
Xiaomeng Huang ◽  
Yufang Ni ◽  
Haohuan Fu ◽  
Guangwen Yang
Keyword(s):  
Data Stream ◽  
Floating Point ◽  
Compression Method ◽  
Point Data

Compression of Barotropic Turbulence Simulation Data Using Wavelet-Based Lossy Coding

2002 ◽  
Author(s):  
John P. Wilson
Keyword(s):  
Evaluation Criteria ◽  
Floating Point ◽  
Simulation Data ◽  
Single Precision ◽  
Acceptable Error ◽  
Turbulence Simulation ◽  
Lossy Coding ◽  
Point Data ◽  
Definition Of ◽  
Quantities Of Interest

Single-precision floating point data from a simulation of barotropic turbulence is compressed with a wavelet-based method. The quantity being compressed is vorticity. The compression error is evaluated both in terms of error in the vorticity and the error in various quantities derived from the vorticity. Numerical error is evaluated in all quantities and visualizations of the vorticity and correlation of the error with the uncompressed data are evaluated. It is found that depending on the quantities of interest and the evaluation criteria, compression ratios of 4:1 to 256:1 are achievable. Under a conservative definition of acceptable error, it is possible to recover quantities of interest from data compressed 4:1 (8bpp), the data rate that in existing practice is used for visualization.

Efficient Multibyte Floating Point Data Formats Using Vectorization

2017 ◽  
Vol 66 (12) ◽  
pp. 2081-2096 ◽  
Author(s):  
Andrew Anderson ◽  
Servesh Muralidharan ◽  
David Gregg
Keyword(s):  
Floating Point ◽  
Data Formats ◽  
Point Data

scholarly journals A fixed-point implementation of tone mapping operation for HDR images expressed in floating-point format

2014 ◽  
Vol 3 ◽  
Author(s):  
Toshiyuki Dobashi ◽  
Atsushi Tashiro ◽  
Masahiro Iwahashi ◽  
Hitoshi Kiya
Keyword(s):  
Fixed Point ◽  
Dynamic Range ◽  
Floating Point ◽  
Tone Mapping ◽  
Fixed Point Arithmetic ◽  
Memory Cost ◽  
Point Data ◽  
Point Arithmetic ◽  
Intermediate Format ◽  
Hdr Image

A tone mapping operation (TMO) for HDR images with fixed-point arithmetic is proposed. A TMO generates a low dynamic range (LDR) image from a high dynamic range (HDR) image by compressing its dynamic range. Since HDR images are generally expressed in a floating-point data format, a TMO also deals with floating-point data even though resulting LDR images have integer data. As a result, conventional TMOs require many resources such as computational and memory cost. To reduce the resources, an integer TMO which treats a floating-point number as two 8-bit integer numbers was proposed. However, this method has the limitation of available input HDR image formats. The proposed method introduces an intermediate format to relieve the limitation of input formats, and expands the integer TMO for the intermediate format. The proposed integer TMO can be applied for multiple formats such as the RGBE and the OpenEXR. Moreover, the method can conduct all calculations in the TMO with fixed-point arithmetic. Using both integer data and fixed-point arithmetic, the method reduces not only the memory cost, but also the computational cost. The experimental and evaluation results show that the proposed method reduces the computational and memory cost, and gives almost same quality of LDR images, compared with the conventional method with floating-point arithmetic.

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