Category: Tips & tricks

Hi folks,

When it comes to IT, we all somehow tend to assume that everything was created and done in the USA.
But this is not really quite so.
And to prove this, today we are going to tell you about just a few French contributions in the hardware/software/internet domains of IT.

The first microcomputer using a microprocessor was invented by a French scientist named Francois Gernelle.
It happened in January 1973, when the Micral N was issued by Gernelle and his team for INRA, the French National Institute for Research in Agronomy (just as a side-note, the first US-made microcomputer was released one year later, in March 1974).
The “Micral N” was able to perform the requested tasks same as minicomputers (at that time the smallest/cheapest available computing devices) but for just a fifth of the price.
Gernelle had designed it around the Intel 8008 microprocessor clocked at 500 kHz, it provided support for serial and parallel input/output, it used punched tape for programming and data storage, used a teleprinter or modem for I/O and costed about 1.700 USD, thus marking the start of the worldwide microcomputers revolution era.
Funny enough, Gernelle built his Micral in a cellar (at Chatenay-Malabry), thus also inaugurating the spirit of IT revolution too, as some famous US players in IT would later commence their breakthrough developments in garages.
Many years after, Gernelle had to confront his ideas against the views of his leadership and refused to develop IBM PC-compatibles. He preferred to quit his job instead of “easily replicating such badly designed hardware and software” as the PC-compatible standard of the time offered “no real multi-tasking nor user-sharing possibilities”, although the existing CPU technologies were providing such potential.

But back in the mid-70’s the Micral continued to be further developed and enhanced and among its users there was this particular French student training his software developing skills on it, named Philippe Kahn.
Grown in Paris and graduating (among other faculties) from the University of Nice, Kahn (engineer, mathematician, musicologist, entrepreneur and innovator) indeed started as developer for the Micral but in 1983 he became president of Borland, a Company that made history in the software industry. Let’s mention just one of their most notable blockbusters: the “Turbo Pascal” language compiler (later to become “Delphi”), which is important to note not only because of its popularity and the many notorious applications that were generated thanks to it, but also because it was technically supervised by a Kahn’s employee named Anders Hejlsberg, none other than the genious guy who later was to conceive and architecture the .NET and the C# at Microsoft.
The above is just a mere exemplification of Kahn’s Borland impact in software industry but let’s add that Kahn also founded Starfish Software, (sold to Motorola for some 325 millions USD in 1998) and Lightsurf Technologies sold to VeriSign in 2005 for 300 millions USD in 2005.
“Lightsurf” was created to monetize Kahn’s invention of the first camera-phone (oh yes!) whose pictures were meant to be shared via the Internet.

Did I just say “Internet”?

Well, then maybe it’s about time to tell you few words about the Minitel, a French pre-WWW service that was so successfull it got discontinued only in 2012, that is 34 years after its release in 1978!
Conceived from its early days to allow its users to make online purchases, make train reservations, check stock prices, search telephone directories, have a mail box, and chat in a similar way to that now are only made made possible by the WWW (we’ve quoted Wikipedia here), the Minitel (abbreviated from the French: Medium Interactif par Numerisation d’Information TELephonique) was such a breakthrough we even don’t know whether we should range it among WWW predecessors or competitors.
So let’s just say that since the end-70’s France was already running a WWW-like service that no other country in the world could ever dream of or compete with but , “hélas!”, it was only adopted internally in France, no real international marketing being done despite the many implementation attempts.

Well, being French and “toutes proportions gardées” of course, we at ORPALIS have developed some creative technologies of our own too, like the Automatic Document Recognition that we provide with the GdPicture.NET SDK or the Automatic Color Detection which empowers the ORPALIS PDF Reducer and PaperScan Pro Edition to mention just a few.

And others are being cooked in our labs now, just hang on with us, we’re doing our best not to deceive you!

So “Vive la France!” and see you next week, folks!


Big Browser on July 5

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Hi folks,

Today we will try to give you a quick overview on the GIF file format.

GIF format was introduced by CompuServe in 1987 and soon became widely adopted because it supported colours (unlike its CompuServe predecessor, RLE) and it was small-sized (thanks to the LZW lossless compression algorithm, we’ve told you a story about that in a previous article), allowing reasonably fast file transfers even for the slow, dial-up modems era.
CompuServe was a company way ahead of its time.
It was founded in Columbus, Ohio, in 1969 (the “prehistory” of the internet) by an insurance company (“Golden United Life Insurance”), initially to provide computing power for in-house optimization of the data-management needs of the mother company but shortly CompuServe became a serious business on its own, some naming it “the Google of the 70’s and 80’s”.
First they rented computing power time to other companies (when computers were idle), then a long series of pioneering achievements followed, here is an incomplete enumeration: one of the first companies to offer online services, world’s first online service offering internet connectivity, issued and hosted thousands of moderated forums, created a file transfer protocol, developed its own (proprietary) email services, hosted the first WYSIWYG email and forum posts content, pioneered the online shopping, created customized portals, pioneered online financial services, created an online chat system, introduced online games and published the first online newspaper and the first online comics.
And invented GIF, of course.

GIF (“Graphics Interchange Format”) is a bitmap graphics file format, its first version being referred to as GIF 87a, to indicate the year of release, 1987.
Two years and many enhancements later, GIF 89a version was introduced and it accounts for the vast majority of GIFs currently existing on the internet.
It provides image designers with 256 colors, allows multiple images storage for animation purposes (not in the sense of multipage, as in TIFF), provides controls for animation (animation speed and single/infinite loop option), allows on-or-off transparency (no intermediary gradients of transparency, as in PNG), provides lossless compression and introduced interlacing as an option.

Interlacing, when it concerns image files, means the image is gradually rendered by a browser but rendering starts immediately after download starts so, at first, the image looks unclear, like being out of focus, then, as its download continues, it becomes sharper and sharper, finally showing in full quality when image download is complete.
Previously described Progressive JPEG and the soon to be described PNG formats provide optional interlacing feature, too, but while in GIF and Progressive JPEG case interlacing changes the rendering order of the horizontal lines, the PNG format allows changing the order both horizontally and vertically.

As for compression, GIF uses the LZW lossless compression algoritm eversince it was created.
LZW was a perfect fit for its 8-bits-per-pixel color encoding (ie, maximum 256 colors displayed at one time in a frame) as well as for the animation feature, because GIF animation works by successively displaying bitmap images slightly different from one another (frames), their most part remaining unchanged so, being repetitive, most data are subject to very efficient compression by LZW.
GIF format isn’t commonly used for photos because compressing only 256 colors, even in a lossless manner, provides much poorer results than lossy compressing some 16 millions colors, like JPEG does.

Although the LZW algorithm was/is a major contributor to GIF’s popularity and widespread, it also gave it almost 10 years of torment (since about 1994 until about 2004) caused by the Unisys patent protection controversy.
But nowadays GIF format enjoys times of peace and recognition which will probably continue to last as long as people will keep their apetite for logos, icons, animated emoticons, low-res short clips, educational animated clips and so forth.
In 2012, this venerable format received public tribute from the Oxford Dictionaries USA subsidiary of Oxford University Press, the word “GIF” being granted the “Word of the Year” title, both as a noun and as a verb.
So now the question is: to GIF or not to GIF ?

We will try to answer this shakespearian question in a future article about which bitmap format fits best for which purpose, as PaperScan supports them all.

See you next week!


Big Browser on May 31

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Hi folks,

As promised in the previous article, today we will try to untangle the frequent confusions generated among many computer users by this simple word : “resolution”.
To do that, we are going to explain screen resolution, printing resolution, pixel dimensions, physical dimensions and how everything relates with regard to bitmaps.

Bitmaps dimensions are expressed in pixels (image width x height) either separated (usually for image files) or as the effective result of the multiplication (usually to express digital cameras sensitivity).
For example, an “8 Megapixels digital camera” means the photos it produces (actually bitmaps) are made up of cca. 8 million pixels, a photo being, for example, 3456 pixels wide by 2304 pixels high, which gives 7,962,624 pixels (cca. 8MP).
Funny thing is that the pixel dimension of the image has little to do with how the bitmap image appears on screen but is has lots to do with how the image will print!
How comes?

Well, to begin with, a pixel is a logical unit, not a physical one.
In other words, a pixel doesn’t have a fixed measured size, its size depends on the screen it is displayed on.
For example, imagine an LCD monitor having a screen resolution of 1000×1000 pixels, the screen’s physical dimensions being 1000×1000 mm.
In such case, the dimension of a pixel will be 1 square milimeter (1×1 mm).
Now imagine that you change the output resolution setting of the same monitor to 500×500 pixels: as monitor’s physical dimensions don’t change, a pixel will now be 2×2 mm, meaning 4 square milimeters.
To continue this exercise of imagination, think about a bitmap image having pixel-dimensions of 1000×1000 pixels: in the first case (screen resolution of 1000×1000) the image will be rendered full screen.
And for the second, case (chaging the screen-resolution of the same monitor to 500×500 pixels), the image will appear bigger (because each pixel is bigger than before) and will be displayed only partially on screen.
Of course, there are no such monitors/resolutions standards in real life, we only gave these examples to show that image size in pixels is relative, not “absolute”.
So perhaps at this point it will be easier to understand what pixels-per-inch (PPI) is : it is a value expressing how many pixels “fit” inside an inch but its significance depends on the context.
-when used with regard to an image resolution, PPI (often reffered to as DPI in this context) is the number of pixels per inch in the bitmap grid and is meant for printers (to determine how the image is to be printed within a specified size);
-when used with regard to a screen resolution’s appearance, PPI is the number of pixels per inch (or pixels-per-centimeter (PPCM) when metric system is used) depending on screen’s physical size and screen’s resolution as set by user;
Here are a few real life facts to help understanding:

-a monitor in 800×600 mode has a lower PPI than the same monitor has in a 1024×768 or 1280×1024 mode;
-a monitor of 12 inches wide (horizontal) by 9 inches height (vertical) at a resolution of 1024×768 pixels has a PPI value of cca. 85 (1024 pixels / 12 inches = 768 pixels / 9 inches = 85.3);
-a monitor on a Windows Operating System typically displays at 96 PPI;
-a bitmap image of 1,000 × 1,000 pixels, if labeled as 250 PPI (DPI is frequently misused to replace PPI in such context), will instruct the printer to print it at a size of 4 × 4 inches.

With printers there’s a different story.
First of all, there is an important difference between how an image appears to the human eye on screen and how it appears when printed.
Due to visual perception physiology, an image don’t need to have a very high resolution in order to appear at a decent size and with good quality on a computer screen.
Unfortunately, this is not the case when it comes to printing it : what is seen on print simply doesn’t match the quality of what is seen on screen.
Screens displays colors out of red, green and blue (RGB color model), by mixing them together into a vast color palette and light is emitted directly to the eyes.
White is obtained when all 3 colors are displayed at full intensity, black is obtained by their absence (zero intensity), hence the name “additive color model” for RGB.
Printers work differently : they create colors by mixing cyan, magenta, yellow and black inks (CMYK color model but there’s also a CMY model), inks absorbe light and human eyes see light reflected from paper.
White is obtained by using none of the 4 colors (as it’s the paper’s color) while black is obtained by full combination of all 4 (or 3, for CMY) colors inks, hence the name “substractive color model” for CMYK.

Similar to how pixel is the picture element of an image on screen, a dot is the picture element of a printed image.
And similar to how PPI value describes the density of pixels on screen, the DPI (“dots-per-inch”) value expresses how many individually printed dots “acomodate” within an inch.
Of course, the higher the DPI value, the better quality of the printed image will be.
But printers have a limited range of colors for each dot and their color pallette is lesser than in the case of screens.
So in order to obtain similar output quality, a bitmap image has to be printed in much higher DPI value than the PPI value needed for good screen viewing.
It is said that the printing process “could require a region of four to six dots (measured across each side) in order to faithfully reproduce the colour contained in a single pixel” so if a 100×100-pixel image is to be printed inside a one inch square, the printer must be capable of 400 to 600 dots per inch in order to accurately reproduce the image.

Finally, as if the already described confusions wouldn’t be enough, DPI is used to also express the resolutions in scanning processes, whereas the correct term to use appears to be “samples-per-inch” (SPI).

Hope that from now on, when using PaperScan it will be easier for you to deal with resolution-related terminology in bitmaps!

See you next week !


Big Browser on May 10

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Hi folks,

As shown in a previous article, all raster (bitmap) files hold information on each and every pixel in the grid that makes an image.
Today we are going to describe some consequences of this storage principle and, hopefully, we will shed some light on some related, “esoteric” terms.

First, all bitmap images are rectangular, as there has to be a grid (vector images, being defined by mathematical equations not by pixels, aren’t “restricted” to rectangular shape)

Secondly, bitmaps are resolution-dependant.
That means the bitmap stores information on a given (fixed) number of pixels only.
If you want to rescale the image to a lower resolution, some pixels from the original grid will have to be discarded while rescaling to higher resolution means new pixels (not existing in the original grid) need to be created.
Down-scaling a bitmap (for example when creating a thumbnail) produces far less damages to the quality of the image as opposed to up-scaling it.
So let’s take a closer look at the up-scaling case: zooming-in a bitmap image (in order to get a better view on details or to enlarge the image) won’t reveal new information at all, because such information simply isn’t there, the bitmap contains information only for the given number of pixels and that’s it.
And beyond a certain threshold, each discretely visible pixel composing the original image will became a bigger and bigger rectangle, altering the image more and more.
To reduce (eliminating is impossible) this upscaling problem, new pixels are created based on original pixels color information and this process of estimating new intermediary values from known existing values is called “interpolation“.
There are several interpolation algorithms, 3 of which are most commonly used: “nearest-neighbour“, bilinear and bicubic interpolations.
Nearest-neighbour is is the most simple but less effective interpolation method, producing jagged edges (looking like stairs) on increased-sized image while bicubic is much more complex and generates smoother straight-lines and curves.
But in the end of the day, no matter their complexity, all algorithms are creating the new pixels by some more or less reasonable aproximation or “educated guess“, so to speak.

Resolution-dependency of bitmaps imposes for another important aspect to be discussed: screen and printer rendering.
This subject contains many confusing terms, some of them commonly misused, so we will dedicate our next blog article entirely to that matter, in an attempt to untangle frequent confusions.

A fourth thing about bitmaps is about converting a bitmap format to another one and is actually good news.
As they are all based on the same encoding principle (storing information on all pixels in the image) conversion between different bitmaps file formats is rather easy.
Most popular bitmap formats are non-proprietary (have open specifications) like PNG, TIFF, JPEG or GIF.
BMP format was invented by Microsoft but being a relatively simple, well documented and free of patents format, it became quite a common raster image format.

See you next week, folks!


Big Browser on April 26

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Casual Friday on April 26

How To Scare Your Neighbors

Hi folks,

This week we are going to tell you a short story about LZW compression and how it influenced 3 widespread graphics file formats: TIFF, GIF and PNG.

The story begins in 1977 and 1978 when israeli computer scientists Abraham Lempel and Jacob Ziv published descriptions of lossless data compression algorithms named LZ-77 and LZ-78, respectively.
Terry Welch, an MIT trained computer scientist, further developed the LZ algorithms and, in 1984, he published the Lempel-Ziv-Welch (LZW) algorithm.

LZW compression became the first widely used universal data compression method on computers.
Being so influential, the “LZ-” based various algorithms became, of course, subject to patent protection in many countries.
For LZW, two patents were issued in the USA (but in other countries as well), the one filed by Welch himself being assigned to Sperry Corporation (Welch’s employer) in 1983.
Sperry Corporation became Unisys in 1986.

Just to have a glimpse on the algorithm, consider this string: “The cat chases the mouse in the room”
The word “the” occurs 3 times.
Replace it by “!” and the string becomes: “! cat chases ! mouse in ! room”.
Add this association (“the” to “!”) to an index and you’ve reduced the length of the string from 36 to 30 characters.
Of course, things are way more complicated than this but the main idea is that the algorithm works very well when there are many repetitive data.
And image files usually contain lots of repetitive data.

In 1986, Aldus Corporation released the first official TIFF specification and in 1988 revision 5.0 was released, which included the ability to use LZW compression.
In 1987, CompuServe created the GIF file format, the GIF specification requiring the use of the LZW algorithm to compress the data stored in each GIF file.

The holder of Welch’s LZW patent, Unisys (which maintains a portfolio of about 1500 patents), was motivated to monetize this patent as much as possible and it had to be fast, too, as the patent availability was 20 years.
Overall, the total number of licensees was about 100, among which Adobe was licensed in 1990 for the use of LZW patent for PostScript and Aldus was licensed in 1991 for the use of the Unisys LZW patent in TIFF.
Licensed LZW in TIFF generated a wave of discontent so Aldus quickly introduced JPEG compression in TIFF (as of revision 6.0 in June 1992) but it had serious design errors and limitations, making things even worse (this was later corrected and replaced with a totally new TIFF/JPEG specification).

But it was not before 1993 that Unisys finally became aware that the GIF file format, very popular already, was using their patent-protected LZW algorithm.
And CompuServe had no clue they were infringing on LZW patent.
In 1994 Unisys and CompuServe came to an understanding which, for various reasons, generated a huge protest reaction, the matter being reported by many newspapers including the Time Magazine.
Many upset developers and users removed their GIF files or converted them to JPEG (yes, JPEG again, it’s royalty-free!).
But JPEG uses lossy compression, so one of the protesting groups, formed by leaders of the online graphics community, began working on a lossless and patent-free version of GIF.
Their efforts produced the PNG specification.

As an epilogue, by 2004 all Welch (ie, Unisys) LZW patents expired in all countries where they were issued (IBM patent on LZW expired by 2006).
TIFF specifications are now controled by Adobe and LZW can be freely used with it.
Adobe uses for PDF a LZ-77 based compression algorithm named “DEFLATE”.
GIF format is still popular.
PNG became one of the most important graphics file formats in the world.

In the upcoming articles we will provide some explanations on bitmaps as well as some short overviews on the most common bitmap file formats, including TIFF, GIF and PNG.

Bye for now!


Big Browser on April 19

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Casual Friday on April 19

A duck family crosses the street