Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Julian Minguillon"'
Autor:
David Megías, Fernando Garcia-Vilchez, Jordi Herrera-Joancomartí, Julian Minguillon, Joan Serra-Sagrista, Jordi Serra
Publikováno v:
IGARSS
This paper describes a study regarding the influence of a watermarking scheme based on near-lossless JPEG2000 compression in the performance of lossless or near-lossless image compression methods. Due to the nature of ultraspectral imaging applicatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0e29ecf75609d5d9a3e35aa010d5fc77
https://doi.org/10.1109/igarss.2005.1526125
https://doi.org/10.1109/igarss.2005.1526125
Publikováno v:
Security and Watermarking of Multimedia Contents
In this paper an empirical evaluation of the lossy compression properties of the JPEG2000 standard for watermarking purposes is performed. The JPEG2000 standard is used as a basic tool for determining both how and where the embedded watermark should
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9d31502fac1a7c96d16f3dcc9e6f0cfb
https://doi.org/10.1117/12.476811
https://doi.org/10.1117/12.476811
Publikováno v:
SPIE Proceedings.
Several well-known methods for lossy compression of still images are here analyzed to evaluate their performance for hyperspectral images. The lossy compression methods discussed are the JPEG standard, and four approaches based on the Wavelet Transfo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::abc7c33cbe9a5ff5ff39901021c3aa49
https://doi.org/10.1117/12.463139
https://doi.org/10.1117/12.463139
Autor:
Julian Minguillon, Joan Serra-Sagrista
Publikováno v:
SPIE Proceedings.
In this paper we study the use of classifier combination for improving the classification accuracy of AVIRIS data. Two types of combination ensembles are used as high-level classifiers, cascading and voting. Regarding the base-level classifiers, we u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fd47fc7deb566d0a254ea0114f680415
https://doi.org/10.1117/12.463145
https://doi.org/10.1117/12.463145
Publikováno v:
SPIE Proceedings.
In this paper several methods for image lossy compression are compared in order to find adaptive schemes that may improve compression performance for hyperspectral images under a classification accuracy constraint. Our goal is to achieve high compres
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b085cde9877676b518fe635db6952893
https://doi.org/10.1117/12.413915
https://doi.org/10.1117/12.413915
Autor:
Julian Minguillon, Jaume Pujol
Publikováno v:
SPIE Proceedings.
In this paper we present a novel method for JPEG standard progressive operation mode definition scripts construction and evaluation. Our method allows the user to construct and evaluate several definition scripts without having to encode and decode a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::241f1bc8bbf7ab3ea4ffcae004bdf259
https://doi.org/10.1117/12.372755
https://doi.org/10.1117/12.372755
Publikováno v:
SPIE Proceedings.
In this paper we present a progressive classification scheme for a document layout recognition system (background, text andimages) using three stages. The first stage, preprocessing. extracts statistical information that may be used for backgrounddet
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4b3897f675a8007369c789edf77b651d
https://doi.org/10.1117/12.351319
https://doi.org/10.1117/12.351319
Autor:
Jaume Pujol, Julian Minguillon
Publikováno v:
SPIE Proceedings.
In this paper we propose a novel method for computing JPEG quantization matrices based on desired mean square error, avoiding the classical trial and error procedure. First, we use a relationship between a Laplacian source and its quantization error
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0b158d1dec7f070556331f6355eb9998
https://doi.org/10.1117/12.330356
https://doi.org/10.1117/12.330356