Rate allocation in the JPEG2000 image compression algorithm is performed by the EBCOT algorithm, measures file size and distortion, defined as mean square error (MSE). Since MSE correlates only mediocre to visual quality, more advanced metrics like the M-SSIM have been proposed. One exploitable effect of the human visual system is that of visual masking: If a structure of a fixed amplitude is overlayed by a texture, it becomes masked and less visible. This can be addressed in JPEG2000 by multiplying the MSE contribution of a codeblock by a factor mu computed from the neighbourhood of the data. Most of these techniques require, however, complex operations on the coefficients.
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%0 Conference Paper
%1 richter2008effective
%A Richter, T.
%B Data Compression Conference, 2008. DCC 2008
%D 2008
%K Gaussian;JPEG2000;Visual Masking allocation;visual coding;Mean coding;Visual coding;mean compression;Distortion compression;image compression;mean data error error;rate evaluation;Signal masking;Data measurement;Humans;Image measurement;Transform methods;JPEG2000;image methods;Performance resolution;Size square system;Generalized
%P 540-540
%R 10.1109/DCC.2008.43
%T Effective Visual Masking Techniques in JPEG2000
%U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4483367
%X Rate allocation in the JPEG2000 image compression algorithm is performed by the EBCOT algorithm, measures file size and distortion, defined as mean square error (MSE). Since MSE correlates only mediocre to visual quality, more advanced metrics like the M-SSIM have been proposed. One exploitable effect of the human visual system is that of visual masking: If a structure of a fixed amplitude is overlayed by a texture, it becomes masked and less visible. This can be addressed in JPEG2000 by multiplying the MSE contribution of a codeblock by a factor mu computed from the neighbourhood of the data. Most of these techniques require, however, complex operations on the coefficients.
@inproceedings{richter2008effective,
abstract = {Rate allocation in the JPEG2000 image compression algorithm is performed by the EBCOT algorithm, measures file size and distortion, defined as mean square error (MSE). Since MSE correlates only mediocre to visual quality, more advanced metrics like the M-SSIM have been proposed. One exploitable effect of the human visual system is that of visual masking: If a structure of a fixed amplitude is overlayed by a texture, it becomes masked and less visible. This can be addressed in JPEG2000 by multiplying the MSE contribution of a codeblock by a factor mu computed from the neighbourhood of the data. Most of these techniques require, however, complex operations on the coefficients.},
added-at = {2016-03-10T09:18:49.000+0100},
author = {Richter, T.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/27f8c25813075df51243507b638c1fc89/thomasrichter},
booktitle = {Data Compression Conference, 2008. DCC 2008},
doi = {10.1109/DCC.2008.43},
interhash = {c6b4d7259fb109715502cb1b7974b2fe},
intrahash = {7f8c25813075df51243507b638c1fc89},
issn = {1068-0314},
keywords = {Gaussian;JPEG2000;Visual Masking allocation;visual coding;Mean coding;Visual coding;mean compression;Distortion compression;image compression;mean data error error;rate evaluation;Signal masking;Data measurement;Humans;Image measurement;Transform methods;JPEG2000;image methods;Performance resolution;Size square system;Generalized},
month = {March},
pages = {540-540},
timestamp = {2016-03-10T08:20:00.000+0100},
title = {{E}ffective {V}isual {M}asking {T}echniques in {JPEG}2000},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4483367},
year = 2008
}
