Luratech Pdf Compression Keygen Download
Elodie Akiyama
LuraTech is a software company, owned since 2015 by Foxit Software, with offices in Remscheid, Berlin, London, and in the United States, which makes products for handling and conversion of digital documents. Its customers are primarily organizations involved in long-term document archiving and scan service providers. It is a member of the PDF Association.[1]
LuraTech was founded as a part of a joint project with the Technical University of Berlin intended to bring wavelet compression techniques to digital still images. LuraTech developed a segmentation technology to deal with scanned documents containing mixed raster content (MRC), resulting in the creation of LuraDocument LDF, a proprietary document format for the compression of scanned documents. Since then, LuraTech has developed several software development kits (SDKs) and computer applications for creating and handling PDF documents. LuraTech has also taken part in the development of the JPEG 2000 standard.[2][3]
In 2002 LuraTech created the PDF Compressor, applying the concepts of MRC layered document compression to PDF standards, especially PDF/A. This was the first workflow solution that LuraTech built on top of its software development kits.
At the 2013 CeBIT conference, LuraTech announced the release of its ZUGFeRD Extraction SDK,[4] a toolkit for ERP and online banking software developers, which facilitates processing of PDF invoices standardized under the Central User Guidelines for Electronic Billing in Germany (ZUGFeRD).
LuraTech serves the Financial, Public, Energy, Legal, and Health Sectors, providing them with highly reliable software solutions needed for automated PDF processing and long-term archiving, as well as document production and workflow platforms. LuraTech solutions focus on unattended creation of or conversion into PDF or PDF/A files from various source formats, including scanned documents but also e-mails, PDF and various office formats, applying its leading compression technology. Document management is an integral necessity across industries, and LuraTech has developed world class tools with sophisticated interfaces to integrate with document management systems.
I'm pretty sure LuraWave is a form of jp2 wavelet compression.
ThumbsPlus now includes support for jp2 & j2k jpeg2000 compression,
so it would have the same advantages as lurawave. But isn't
LuraDocument cool?
"To take advantage of a JPEG2000, web browsers will need a Plug-In for either Internet Explorer or Netscape browsers. These free plug-in's are expected to be available later this year. The extension for the new files will be ".jp2"." "
"To take advantage of a JPEG2000, web browsers will need a Plug-In
for either Internet Explorer or Netscape browsers. These free
plug-in's are expected to be available later this year. The
extension for the new files will be ".jp2"." "
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The LuraTech PDF Compressor Enterprise is the professional solution for process-oriented document conversion and compression. This flexible, scalable solution is designed for processing data volumes of any size.
Scanned documents are compressed into PDF- or PDF/A-format files and made searchable using OCR (character recognition). The Born Digital Module also allows you to process batches of digital documents created in MS Office, PDF or Outlook email format (including attachments) and to convert them to PDF or PDF/A.
For years, scan service providers and other companies, organizations and institutions dealing with large numbers of incoming documents have used the LuraTech PDF Compressor. From capture to output, it reliably and automatically structures scanned and born-digital documents alike.
The LuraTech PDF Compressor is used in thousands of active installations for all kinds of centralized mass-processing projects: everything from small jobs to huge contracts converting millions of pages as quickly as possible.
As a production- and process-oriented solution, the LuraTech PDF Compressor can be easily added into any existing workflow. Integration types include batch processing and job-list processing, in which the LuraTech PDF Compressor Enterprise is jointly controlled by both upstream and downstream processes.
table tr:nth-child(2n) background-color: #f8f8f8;Some time ago Will Palmer, Peter May and Peter Cliff of the British Library published a really interesting paper that investigated three different JPEG 2000 codecs, and their effects on image quality in response to lossy compression. Most remarkably, their analysis revealed differences not only in the way these codecs encode (compress) an image, but also in the decoding phase. In other words: reading the same lossy JP2 produced different results depending on which implementation was used to decode it.
A limitation of the paper's methodology is that it obscures the individual effects of the encoding and decoding components, since both are essentially lumped in the analysis. Thus, it's not clear how much of the observed degradation in image quality is caused by the compression, and how much by the decoding. This made me wonder how similar the decode results of different codecs really are.
Note that, unlike the table in the previous section, these PSNR values are only a measure of the similarity between the different decoder results. They don't directly say anything about quality (since we're not comparing against the source image). Interestingly, the PSNR values in the matrix show two clear groups:
What this means is that OpenJPEG, Irfanview, ImageMagick and Kakadu in precise mode all decode the image in a similar way, whereas Kakadu (default mode) and GraphicsMagick behave differently. Another way of looking at this is to count the pixels that have different values for each combination. This yields up to 2 % different pixels for all combinations in group A, and about 12 % in group B. Finally, we can look at the peak absolute error value (PAE) of each combination, which is the maximum value difference for any pixel in the image. This figure was 1 pixel level (0.4 % of the full range) in both groups.
I also repeated the above procedure for a small RGB image. In this case I used Kakadu as the encoder. The decoding results of that experiment showed the same overall pattern, although the differences between groups A and B were even more pronounced, with PAE values in group B reaching up to 3 pixel values (1.2 % of full range) for some decoder combinations.
It would be tempting to conclude from this that the codecs that make up group A provide better quality decoding than the others (GraphicsMagick, Kakadu in default mode). If this were true, one would expect that the overall PSNR values relative to the source TIFF (see previous table) would be higher for those codecs. But the values in the table are only marginally different. Also, in the test on the small RGB image, running Kakadu in precise mode lowered the overall PSNR value (although by a tiny amount). Such small effects could be due to chance, and for a conclusive answer one would need to repeat the experiment for a large number of images, and test the PSNR differences for statistical significance (as was done in the BL analysis).
I'm still somewhat surprised that even in group A the decoding results aren't identical, but I suspect this has something to do with small rounding errors that arise during the decode process (maybe someone with a better understanding of the mathematical intricacies of JPEG 2000 decoding can comment on this). Overall, these results suggest that the errors that are introduced by the decode step are very small when compared against the encode errors.
OpenJPEG, (recent versions of) ImageMagick, IrfanView and Kakadu in precise mode all produce similar results when decoding lossily compressed JP2s, whereas Kakadu in default mode and GraphicsMagick (which uses the JasPer library) behave differently. These differences are very small when compared to the errors that are introduced by the encoding step, but for critical decode applications (migrate lossy JP2 to something else) they may still be significant. As both ImageMagick and GraphicsMagick are often used for calculating image (quality) statistics, the observed differences also affect the outcome of such analyses: calculating PSNR for a JP2 with ImageMagick and GraphicsMagick results in two different outcomes!
This tentative analysis does not support any conclusions on which decoders are 'better'. That would need additional tests with more images. I don't have time for that myself, but I'd be happy to see others have a go at this!
Thanks Andy and Chris, I was thinking along similar lines myself. Years ago I used to work on the development of a hydrological simulation model, and I remember running into similar issues related to floating point arithmetic and rounding errors. In any case the errors introduced by the decode process are so small (esp. relative to the encode errors) that I suppose they're negligible for most practical purposes. In does make me wonder about this conclusion and recommendation from the BL paper:
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