Now let's look at exactly how we go about storing an image in a GIFfile. The GIF format is a raster format, meaning it stores image databy remembering the color of every pixel in the image. Morespecifically, GIF files remember the index of the color in a colortable for each pixel. To make that clearer, let's review thesample image we used in the firstsection.
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The color table came from the global color table block. The colorsare listed in the order which they appear in the file. The first coloris given an index of zero. When we send the codes, we always start atthe top left of the image and work our way right. When we get to theend of the line, the very next code is the one that starts the nextline. (The decoder will 'wrap' the image based on the imagedimensions.) We could encode our sample image in the followingway:
1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1,1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2, 1, 1, 1,0, 0, 0, 0, 2, 2, 2, ..
The above listing shows the sequence required to render the first fivelines of the image. We could continue with this method until we'vespecified the color for every pixel; however, this can result in arather large file. Luckily for us, the GIF format allows us to takeadvantage of repetition in our output and to compress our data.
Much of the following information came from John Barkaus's tutorialLZW and GIF Explained, which seems to have fallen off theweb. I've tried to provide more detailed samples as wellas illustrations to make the process even clearer
LZW Compression
The compression method GIF use is a variant of LZW(Lempel-Ziv-Welch) compression. To start using this method, we need acode table. This code table will allow us to usespecial codes to indicate a sequence of colors rather than just one ata time. The first thing we do is to initialize the codetable. We start by adding a code for each of the colors in thecolor table. This would be a local color table if one was provided, orthe global color table. (I will be starting all codes with'#' to distinguish them from color indexes.)
I added a code for each of the colors in the global color table ofour sample image. I also snuck in two special control codes. (Thesespecial codes are only used in the GIF version of LZW, not in standardLZW compression.) Our code table is now considered initialized.
Let me now explain what those special codes are for. The first new code is the clear code (CC). Whenever you come across the clear code in the image data, it's your cue to reinitialize the code table. (I'll explain why you might need to do this in a bit.) The second new code is the end of information code (EOI). When you come across this code, this means you've reached the end of the image. Here I've placedthe special codes right after the color codes, but actually the value ofthe special codes depends on the value of the LZW minimum code sizefrom the image data block. If the LZW minimum code size is the same asthe color table size, then special codes immediatly follow the colors; howeverit is possible to specify a larger LWZ minimum code size which may leavea gap in the codes where no colors are assigned. This can besummarizaed in the following table.
Before we proceed, let me define two more terms. First the index stream will be the list of indexes of the color for each of the pixels. This is the input we will be compressing. The code stream will be the list of codes we generate as output. Theindex buffer will be the list of color indexeswe care 'currently looking at.' The index buffer will contain a listof one or more color indexes. Now we can step though the LZW compression algorithm. First, I'll just list the steps. After thatI'll walk through the steps with our specific example.
Seems simple enough, right? It really isn't all that bad. Let'swalk though our sample image to show you how this works. (The steps Iwill be describing are summarized in the following table. Numbershighlighted in green are in the index buffer; numbers in purple arethe current K value.) We have already initialized our code table. Westart by doing two things: we output our clear code (#4) to the codestream, and we read the first color index from the index stream, 1,into our index buffer [Step 0].
Now we enter the main loop of the algorithm. We read the next indexin the index stream, 1, into K [Step 1]. Next we see if we have arecord for the index buffer plus K in the code stream. In this case welooking for 1,1. Currently our code table only contains single colorsso this value is not in there. Now we will actually add a new row toour code table that does contain this value. The next available codeis #6, we will let #6 be 1,1. Note that we do not actually send thiscode to the code stream, instead we send just the code for thevalue(s) in the index buffer. The index buffer is just 1 and the codefor 1 is #1. This is the code we output. We now reset the index bufferto just the value in K and K becomes nothing. [Step 2].
We continue by reading the next index into K. [Step 3]. Now K is 1 and theindex buffer is 1. Again we look to see if there is a value in our codetable for the buffer plus K (1,1) and this time there is. (In fact we justadded it.) Therefore we add K to the end of the index buffer and clear outK. Now our index buffer is 1,1. [Step 4].
The next index in the index stream is yet another 1. This is ournew K [Step 5]. Now the index buffer plus K is 1,1,1 which we do nothave a code for in our code table. As we did before, we define a newcode and add it to the code table. The next code would be #7; thus #7= 1, 1, 1. Now we kick out the code for just the values in the indexbuffer (#6 = 1,1) to the code stream and set the index buffer to beK. [Step 6].
I've included a few more steps to help you see the pattern. Youkeep going until you run out of indexes in the index stream. Whenthere is nothing new to read, you simply write out the code forwhatever values you may have in your index buffer. Finally you shouldsend the end-of-information code to the code stream. In this example,that code is #5. (View the complete code table.)
As you can see we dynamically built many new codes for our codetable as we compressed the data. For large files this can turn into alarge number of codes. It turns out that the GIF format specifies amaximum code of #4095 (this happens to be the largest 12-bitnumber). If you want to use a new code, you have to clear out all ofyour old codes. You do this by sending the clear code (which for oursample was the #4). This tells the decoder that you are reinitializingyour code table and it should too. Then you start building your owncodes again starting just after the value for your end-of-informationcode (in our sample, we would start again at #6).
The final code stream would look like this:
#4 #1 #6 #6 #2 #9 #9 #7 #8 #10 #2 #12 #1 #14 #15 #6 #0 #21 #0 #10 #7 #22 #23 #18 #26 #7 #10 #29 #13 #24 #12 #18 #16 #36 #12 #5
This is only 36 codes versus the 100 that would be required without compression.
LZW Decompression
At some point we will need to turn this code stream back intoa picture. To do this, we only need to know the values in the streamand the size of the color table that was used. That's it. You remember thatbig code table we built during compression? We actually have enough informationin the code stream itself to be able to rebuild it.
Again, i'll list the algorithm and then we will walk though an example. Letme define a few terms i will be using. CODE will be current code we're working with. CODE-1 will be the code just before CODE in the code stream. {CODE} will be the value for CODE in the code table. For example, using the codetable we created during compression, if CODE=#7 then {CODE}=1,1,1. In the same way, {CODE-1} would be the value in the code table for the code that came before CODE. Looking at step 26 from the compression, if CODE=#7, then {CODE-1} would be {#9}, not {#6}, which was 2,2.
Let's start reading though the code stream we've created to show how to turn it back into a list of color indexes. The first value in the code stream should be a clear code. This means we should initialize our code table. To do this we must know how many colors are in our color table. (This information comes from the first byte in the image data block in the file. More on this later.) Again we will set up codes #0-#3 to be each of the four colors and add in the clear code (#4) and end of information code (#5).
The next step is to read the first color code. In the following table you will see the values of CODE highlighted in purple, and the values forCODE-1 highlighted in green. Our first CODE value is #1. We then output{#1}, or simply 1, to the index stream [Step 0].
Now we enter the main loop of the algorithm. The next code gets assignedto CODE which now makes that value #6. Next we check to see if this valueis in our code table. At this time, it is not. This means we must find the first index in the value of {CODE-1} and call this K. Thus K = first index of{CODE-1} = first index of {#1} = 1. Now we output {CODE-1} + K to the index stream and add this value to our code table. The means we output 1,1 and give this value a code of #6 [Step 1].
We start the loop again by reading the next code. CODE now would be#6 and this time we do have a record for this code in our codetable. Thus we dump {#6} to the index stream which would be 1,1.Now we take the first index in {#6} and call that K. Here, {#6} hastwo indexes, the first of which is 1; thus K = 1. Before movingon, we add {CODE-1}+K to the code table. This #7 is now 1, 1, 1 [Step 2].
I've included a few more steps so you can see the algorithm in action. Whilethe explanation may sound complicated, you can see it's actually quite simple.You'll also notice that you end up building the exact same code tableas the one that was created during compression. This is the reason thatLZW is so great; we can just share the codes and not the table.
Saving the Code Stream as Bytes
I've shown you how to go back and forth between index and code stream, buthaven't told you what to do with them. The index stream is used to specify thecolor of each of the pixel of your image and really only shows up on screen.It is the code stream that is actually saved in the GIF files on your computer or transmitted over the internet. In order to save these code streams, we mustturn them into bytes. The first thought might be to store each of the codesas its own byte; however this would limit the max code to just #255 and result in a lot of wasted bits for the small codes. To solve these problems,the GIF file format actually uses flexible code sizes.
Flexible code sizes allow for further compression by limiting the bitsneeded to save the code stream as bytes. The code size is the number of bits it takes to store the value of the code. When we talk about bits, we're referring to the 1's and 0's that make up a byte. The codes are converted to their binary values to come up with the bits. To specify the code for #4, you would look at this binary equivalent, which is 100, and see that you would need three bits to store this value. The largest codevalue in our sample code stream is #36 (binary: 100100) which would take 6 bits to encode. Note that the number of bits i've just given is the minimum number. You can make the number take up more bits by addingzeros to the front.
We need a way to know what size each of the codes are. Recall that the image data block begins with a single byte value called the LZW minimum code size. The GIF format allows sizes as smallas 2 bits and as large as 12 bits. This minimum code size value is typicallythe number of bits/pixel of the image. So if you have 32 colors in your image,you will need 5 bits/pixel (for numbers 0-31 because 31 in binary is 11111). Thus, this will most likely be one more than the bit value for the size of the color table you are using. (Even if you only have two colors, the minimumcode size most be at least 2.) Refer to the code table above to remind yourself how that works.
Here's the funny thing: the value for minimum code size isn'tactually the smallest code size that's used in the encodingprocess. Because the minimum code size tells you how many bits areneeded just for the different colors of the image, you still have toaccount for the two special codes that we always add to the codetable. Therefore the actual smallest code size that will be used isone more than the value specified in the 'minimum' code sizebyte. I'll call this new value the first code size.
We now know how many bytes the first code will be. This size will probably work for the next few codes as well, but recall that the GIF formatallows for flexible code sizes. As larger code values get added to your code table, you will soon realize that you need larger code sizes if you were to output those values. When you are encoding the data, you increaseyour code size as soon as your write out the code equal to 2^(current code size)-1. If you are decoding from codes to indexes,you need to increase your code size as soon as you add the code value thatis equal to 2^(current code size)-1 to your code table. That is, the next time you grab the next section of bits, you grab one more.
Note that the largest code size allowed is 12 bits. If you get to thislimit, the next code you encounter should be the clear code whichwould tell you to reinitialize the code table. You then go back to using the first code size and grow again when necessary.
Jumping back to our sample image, we see that we have a minimum codesize value of 2 which means out first code size will be 3 bits long. Out first three codes, #1 #6 and #6, would be coded as 001 110 and 110.If you see at Step 6 of the encoding, we added a code of #7 to our codetable. This is our clue to increase our code size because 7 is equal to2^3-1 (where 3 is our current code size). Thus, the next code we write out, #2, will use the new code size of 4 and therefore looklike 0010. In the decoding process, we again would increase our codesize when we read the code for #7 and would read the next 4, rather thanthe next 3 bits, to get the next code. In the sample table above thisoccurs in Step 2.
Finally we must turn all these bit values into bytes. The lowest bit of thecode bit value gets placed in the lowest available bit of the byte. Afteryou've filled up the 8 bits in the byte, you take any left over bits and start a new byte. Take a look at the following illustration to seehow that works with the codes from our sample image.
You can see in the first byte that was returned (8C) that the lowest three bits (because that wasour first code size) contain 110 which is the binary value of 4 sothat would be the clear code we started with, #4. In the three bits tothe left, you see 001 which out or first data code of #1. You can alsosee when we switched into code sizes of 4 bits in the second byte(2D).
When you run out of codes but have filled less than 8 bits of thebyte, you should just fill the remaining bits with zeros. Recall thatthe image data must be broken up onto data sub-blocks. Eachof the data sub-blocks begins with a byte that specifies how manybytes of data. The value will be between 1 and 255. After you readthose bytes, the next byte indicates again how many bytes of datafollow. You stop when you encounter a subblock that has a lenght ofzero. That tells you when you've reached the end of the image data. Inour sample the image the byte just after the LZW code size is 16 which indicates that 22 bytes of datafollow. After we reach those, we see the next byte is 00 which means we are all done.
Return codes from bytes the basically just the same process inreverse. A sample illustration of the process follows which shows howyou would extract codes if the first code size were 5 bits.
Next: Animation and Transparency
That is pretty much everything you need to know to read or generate a basic image file. One of the reasons the GIF becames such a popularformat was because it also allowed for 'fancier' features. Thesefeatures include animation and transparency. Next we'll look at how those work.
Image file formats are standardized means of organizing and storing digital images. An image file format may store data in an uncompressed format, a compressed format (which may be lossless or lossy), or a vector format. Image files are composed of digital data in one of these formats so that the data can be rasterized for use on a computer display or printer. Rasterization converts the image data into a grid of pixels. Each pixel has a number of bits to designate its color (and in some formats, its transparency). Rasterizing an image file for a specific device takes into account the number of bits per pixel (the color depth) that the device is designed to handle.
Image file sizes[edit]
The size of raster image files is positively correlated with the number of pixels in the image and the color depth (bits per pixel). Images can be compressed in various ways, however. A compression algorithm stores either an exact representation or an approximation of the original image in a smaller number of bytes that can be expanded back to its uncompressed form with a corresponding decompression algorithm. Images with the same number of pixels and color depth can have very different compressed file size. Considering exactly the same compression, number of pixels, and color depth for two images, different graphical complexity of the original images may also result in very different file sizes after compression due to the nature of compression algorithms. With some compression formats, images that are less complex may result in smaller compressed file sizes. This characteristic sometimes results in a smaller file size for some lossless formats than lossy formats. For example, graphically simple images (i.e. images with large continuous regions like line art or animation sequences) may be losslessly compressed into a GIF or PNG format and result in a smaller file size than a lossy JPEG format.
For example, a 640 * 480 pixel image with 24-bit color would occupy almost a megabyte of space:
640 * 480 * 24 = 7,372,800 bits = 921,600 bytes = 900 KiB
With vector images the file size increases only with the addition of more vectors.
Image file compression[edit]
There are two types of image file compression algorithms: lossless and lossy.
Lossless compression Simple keys 2 5 50. algorithms reduce file size while preserving a perfect copy of the original uncompressed image. Lossless compression generally, but not always, results in larger files than lossy compression. Lossless compression should be used to avoid accumulating stages of re-compression when editing images.
Lossy compression algorithms preserve a representation of the original uncompressed image that may appear to be a perfect copy, but it is not a perfect copy. Often lossy compression is able to achieve smaller file sizes than lossless compression. Most lossy compression algorithms allow for variable compression that trades image quality for file size.
Major graphic file formats[edit]Pic To Gif 1.1.0 Computer
Including proprietary types, there are hundreds of image file types. The PNG, JPEG, and GIF formats are most often used to display images on the Internet. Some of these graphic formats are listed and briefly described below, separated into the two main families of graphics: raster and vector.
In addition to straight image formats, Metafile formats are portable formats which can include both raster and vector information. Examples are application-independent formats such as WMF and EMF. The metafile format is an intermediate format. Most applications open metafiles and then save them in their own native format. Page description language refers to formats used to describe the layout of a printed page containing text, objects and images. Examples are PostScript, PDF and PCL.
Raster formats[edit]JPEG/JFIF[edit]
JPEG (Joint Photographic Experts Group) is a lossy compression method; JPEG-compressed images are usually stored in the JFIF (JPEG File Interchange Format) file format. The JPEG/JFIF filename extension is JPG or JPEG. Nearly every digital camera can save images in the JPEG/JFIF format, which supports eight-bit grayscale images and 24-bit color images (eight bits each for red, green, and blue). JPEG applies lossy compression to images, which can result in a significant reduction of the file size. Applications can determine the degree of compression to apply, and the amount of compression affects the visual quality of the result. When not too great, the compression does not noticeably affect or detract from the image's quality, but JPEG files suffer generational degradation when repeatedly edited and saved. (JPEG also provides lossless image storage, but the lossless version is not widely supported.)
JPEG 2000[edit]
JPEG 2000 is a compression standard enabling both lossless and lossy storage. The compression methods used are different from the ones in standard JFIF/JPEG; they improve quality and compression ratios, but also require more computational power to process. JPEG 2000 also adds features that are missing in JPEG. It is not nearly as common as JPEG, but it is used currently in professional movie editing and distribution (some digital cinemas, for example, use JPEG 2000 for individual movie frames).
Exif[edit]![]()
The Exif (Exchangeable image file format) format is a file standard similar to the JFIF format with TIFF extensions; it is incorporated in the JPEG-writing software used in most cameras. Its purpose is to record and to standardize the exchange of images with image metadata between digital cameras and editing and viewing software. The metadata are recorded for individual images and include such things as camera settings, time and date, shutter speed, exposure, image size, compression, name of camera, color information. When images are viewed or edited by image editing software, all of this image information can be displayed.
The actual Exif metadata as such may be carried within different host formats, e.g. TIFF, JFIF (JPEG) or PNG. IFF-META is another example.
TIFF[edit]
The TIFF (Tagged Image File Format) format is a flexible format that normally saves eight bits or sixteen bits per color (red, green, blue) for 24-bit and 48-bit totals, respectively, usually using either the TIFF or TIF filename extension. The tagged structure was designed to be easily extendible, and many vendors have introduced proprietary special-purpose tags â with the result that no one reader handles every flavor of TIFF file.[citation needed] TIFFs can be lossy or lossless, depending on the technique chosen for storing the pixel data. Some offer relatively good lossless compression for bi-level (black&white) images. Some digital cameras can save images in TIFF format, using the LZW compression algorithm for lossless storage. TIFF image format is not widely supported by web browsers. TIFF remains widely accepted as a photograph file standard in the printing business. TIFF can handle device-specific color spaces, such as the CMYK defined by a particular set of printing press inks. OCR (Optical Character Recognition) software packages commonly generate some form of TIFF image (often monochromatic) for scanned text pages.
GIF[edit]
The GIF (Graphics Interchange Format) is in normal use limited to an 8-bit palette, or 256 colors (while 24-bit color depth is technically possible).[1][2] GIF is most suitable for storing graphics with few colors, such as simple diagrams, shapes, logos, and cartoon style images, as it uses LZW lossless compression, which is more effective when large areas have a single color, and less effective for photographic or dithered images. Due to GIF's simplicity and age, it achieved almost universal software support. Due to its animation capabilities, it is still widely used to provide image animation effects, despite its low compression ratio compared to modern video formats.
BMP[edit]
The BMP file format (Windows bitmap) handles graphic files within the Microsoft Windows OS. Typically, BMP files are uncompressed, and therefore large and lossless; their advantage is their simple structure and wide acceptance in Windows programs.
PNG[edit]
The PNG (Portable Network Graphics) file format was created as a free, open-source alternative to GIF. The PNG file format supports eight-bit paletted images (with optional transparency for all palette colors) and 24-bit truecolor (16 million colors) or 48-bit truecolor with and without alpha channel - while GIF supports only 256 colors and a single transparent color.
Compared to JPEG, PNG excels when the image has large, uniformly colored areas. Even for photographs â where JPEG is often the choice for final distribution since its compression technique typically yields smaller file sizes â PNG is still well-suited to storing images during the editing process because of its lossless compression.
PNG provides a patent-free replacement for GIF (though GIF is itself now patent-free), and can also replace many common uses of TIFF. Indexed-color, grayscale, and truecolor images are supported, plus an optional alpha channel. The Adam7 interlacing allows an early preview, even when only a small percentage of the image data has been transmitted. PNG can store gamma and chromaticity data for improved color matching on heterogeneous platforms.
PNG is designed to work well in online viewing applications like web browsers and can be fully streamed with a progressive display option. PNG is robust, providing both full file integrity checking and simple detection of common transmission errors.
Animated formats derived from PNG are MNG and APNG, which is backwards compatible with PNG and supported by most browsers.
PPM, PGM, PBM, and PNM[edit]
Netpbm format is a family including the portable pixmap file format (PPM), the portable graymap file format (PGM) and the portable bitmap file format (PBM). These are either pure ASCII files or raw binary files with an ASCII header that provide very basic functionality and serve as a lowest common denominator for converting pixmap, graymap, or bitmap files between different platforms. Several applications refer to them collectively as PNM ('Portable aNy Map').
WebP[edit]
WebP is a new open image format that uses both lossless and lossy compression. It was designed by Google to reduce image file size to speed up web page loading: its principal purpose is to supersede JPEG as the primary format for photographs on the web. WebP is based on VP8's intra-frame coding and uses a container based on RIFF.
HDR raster formats[edit]
Most typical raster formats cannot store HDR data (32 bit floating point values per pixel component), which is why some relatively old or complex formats are still predominant here, and worth mentioning separately. Newer alternatives are showing up, though. RGBE is the format for HDR images originating from Radiance and also supported by Adobe Photoshop. JPEG-HDR is a file format from Dolby Labs similar to RGBE encoding, standardized as JPEG XT Part 2.
JPEG XT Part 7 includes support for encoding floating point HDR images in the base 8-bit JPEG file using enhancement layers encoded with four profiles (A-D); Profile A is based on the RGBE format and Profile B on the XDepth format from Trellis Management.
HEIF[edit]
The High Efficiency Image File Format (HEIF) is an image container format that was standardized by MPEG on the basis of the ISO base media file format. While HEIF can be used with any image compression format, the HEIF standard specifies the storage of HEVC intra-coded images and HEVC-coded image sequences taking advantage of inter-picture prediction.
BAT[edit]
BAT was released into the public domain by C-Cube Microsystems. The 'official' file format for JPEG files is SPIFF (Still Picture Interchange File Format), but by the time it was released, BAT had already achieved wide acceptance. SPIFF, which has the ISO designation 10918-3, offers more versatile compression, color management, and metadata capacity than JPEG/BAT, but it has little support. It may be superseded by JPEG 2000/DIG 2000: ISO SC29/WG1, JPEG - Information Links. Digital Imaging Group, 'JPEG 2000 and the DIG: The Picture of Compatibility.'
Other raster formats[edit]
Container formats of raster graphics editors[edit]
These image formats contain various images, layers and objects, out of which the final image is to be composed
Vector formats[edit]
As opposed to the raster image formats above (where the data describes the characteristics of each individual pixel), vector image formats contain a geometric description which can be rendered smoothly at any desired display size.
At some point, all vector graphics must be rasterized in order to be displayed on digital monitors. Vector images may also be displayed with analog CRT technology such as that used in some electronic test equipment, medical monitors, radar displays, laser shows and early video games. Plotters are printers that use vector data rather than pixel data to draw graphics.
CGM[edit]
CGM (Computer Graphics Metafile) is a file format for 2D vector graphics, raster graphics, and text, and is defined by ISO/IEC 8632. All graphical elements can be specified in a textual source file that can be compiled into a binary file or one of two text representations. CGM provides a means of graphics data interchange for computer representation of 2D graphical information independent from any particular application, system, platform, or device.It has been adopted to some extent in the areas of technical illustration and professional design, but has largely been superseded by formats such as SVG and DXF.
Gerber format (RS-274X)[edit]Pic To Gif 1.1.0 Download
The Gerber format (aka Extended Gerber, RS-274X) was developed by Gerber Systems Corp., now Ucamco, and is a 2D bi-level image description format. It is the de facto standard format used by printed circuit board or PCB software. It is also widely used in other industries requiring high-precision 2D bi-level images.[3]
SVG[edit]Pic To Gif 1.1.0 Apk![]()
SVG (Scalable Vector Graphics) is an open standard created and developed by the World Wide Web Consortium to address the need (and attempts of several corporations) for a versatile, scriptable and all-purpose vector format for the web and otherwise. The SVG format does not have a compression scheme of its own, but due to the textual nature of XML, an SVG graphic can be compressed using a program such as gzip. Because of its scripting potential, SVG is a key component in web applications: interactive web pages that look and act like applications.
Other 2D vector formats[edit]
Pic To Gif 1.1.0 File3D vector formats[edit]Pic To Gif 1.1.0 Converter
Compound formats[edit]
These are formats containing both pixel and vector data, possible other data, e.g. the interactive features of PDF.
Pic To Gif 1.1.0 Free
Stereo formats[edit]
References[edit]
Pic To Gif 1.1.0 Minecraft
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