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New Content Packs are added regularly. On April 23, , a judge presiding over the two lawsuits in Chicago, Illinois granted the motions in those cases. Beginning in and continuing as of early , an entity known as Princeton Digital Image Corporation, [39] based in Eastern Texas, began suing large numbers of companies for alleged infringement of U.
Patent 4,, Princeton claims that the JPEG image compression standard infringes the ' patent and has sued large numbers of websites, retailers, camera and device manufacturers and resellers.
The patent was originally owned and assigned to General Electric. The patent expired in December , but Princeton has sued large numbers of companies for "past infringement" of this patent. Under U.
As of March , Princeton had suits pending in New York and Delaware against more than 55 companies. General Electric's involvement in the suit is unknown, although court records indicate that it assigned the patent to Princeton in and retains certain rights in the patent.
The JPEG compression algorithm operates at its best on photographs and paintings of realistic scenes with smooth variations of tone and color. For web usage, where reducing the amount of data used for an image is important for responsive presentation, JPEG's compression benefits make JPEG popular.
However, JPEG is not well suited for line drawings and other textual or iconic graphics, where the sharp contrasts between adjacent pixels can cause noticeable artifacts.
The JPEG standard includes a lossless coding mode, but that mode is not supported in most products. As the typical use of JPEG is a lossy compression method, which reduces the image fidelity, it is inappropriate for exact reproduction of imaging data such as some scientific and medical imaging applications and certain technical image processing work.
JPEG is also not well suited to files that will undergo multiple edits, as some image quality is lost each time the image is recompressed, particularly if the image is cropped or shifted, or if encoding parameters are changed — see digital generation loss for details.
To prevent image information loss during sequential and repetitive editing, the first edit can be saved in a lossless format, subsequently edited in that format, then finally published as JPEG for distribution. A perceptual model based loosely on the human psychovisual system discards high-frequency information, i. In the transform domain, the process of reducing information is called quantization.
In simpler terms, quantization is a method for optimally reducing a large number scale with different occurrences of each number into a smaller one, and the transform-domain is a convenient representation of the image because the high-frequency coefficients, which contribute less to the overall picture than other coefficients, are characteristically small-values with high compressibility.
The quantized coefficients are then sequenced and losslessly packed into the output bitstream. Nearly all software implementations of JPEG permit user control over the compression ratio as well as other optional parameters , allowing the user to trade off picture-quality for smaller file size. In embedded applications such as miniDV, which uses a similar DCT-compression scheme , the parameters are pre-selected and fixed for the application.
The compression method is usually lossy , meaning that some original image information is lost and cannot be restored, possibly affecting image quality. There is an optional lossless mode defined in the JPEG standard. However, this mode is not widely supported in products. There is also an interlaced progressive JPEG format, in which data is compressed in multiple passes of progressively higher detail.
This is ideal for large images that will be displayed while downloading over a slow connection, allowing a reasonable preview after receiving only a portion of the data. However, support for progressive JPEGs is not universal. When progressive JPEGs are received by programs that do not support them such as versions of Internet Explorer before Windows 7 [41] the software displays the image only after it has been completely downloaded.
There are also many medical imaging, traffic and camera applications that create and process bit JPEG images both grayscale and color. The libjpeg codec supports bit JPEG and there even exists a high-performance version. Several alterations to a JPEG image can be performed losslessly that is, without recompression and the associated quality loss as long as the image size is a multiple of 1 MCU block Minimum Coded Unit usually 16 pixels in both directions, for chroma subsampling.
Utilities that implement this include:. Blocks can be rotated in degree increments, flipped in the horizontal, vertical and diagonal axes and moved about in the image. Not all blocks from the original image need to be used in the modified one.
This limits the possible lossless crop operations, and also prevents flips and rotations of an image whose bottom or right edge does not lie on a block boundary for all channels because the edge would end up on top or left, where — as aforementioned — a block boundary is obligatory.
Rotations where the image is not a multiple of 8 or 16, which value depends upon the chroma subsampling, are not lossless. Rotating such an image causes the blocks to be recomputed which results in loss of quality. When using lossless cropping, if the bottom or right side of the crop region is not on a block boundary, then the rest of the data from the partially used blocks will still be present in the cropped file and can be recovered.
It is also possible to transform between baseline and progressive formats without any loss of quality, since the only difference is the order in which the coefficients are placed in the file.
Furthermore, several JPEG images can be losslessly joined, as long as they were saved with the same quality and the edges coincide with block boundaries. However, this "pure" file format is rarely used, primarily because of the difficulty of programming encoders and decoders that fully implement all aspects of the standard and because of certain shortcomings of the standard:. Several additional standards have evolved to address these issues. Within these segments of the file that were left for future use in the JIF standard and are not read by it, these standards add specific metadata.
Thus, in some ways, JFIF is a cut-down version of the JIF standard in that it specifies certain constraints such as not allowing all the different encoding modes , while in other ways, it is an extension of JIF due to the added metadata. The documentation for the original JFIF standard states: [44]. Nor should it, for the only purpose of this simplified format is to allow the exchange of JPEG compressed images.
Most image capture devices such as digital cameras that output JPEG are actually creating files in the Exif format, the format that the camera industry has standardized on for metadata interchange.
This allows older readers to correctly handle the older format JFIF segment, while newer readers also decode the following Exif segment, being less strict about requiring it to appear first. The most common filename extensions for files employing JPEG compression are. Because these color spaces use a non-linear transformation, the dynamic range of an 8-bit JPEG file is about 11 stops ; see gamma curve. If the image doesn't specify color profile information untagged , the color space is assumed to be sRGB for the purposes of display on webpages.
A JPEG image consists of a sequence of segments , each beginning with a marker , each of which begins with a 0xFF byte, followed by a byte indicating what kind of marker it is. Some markers consist of just those two bytes; others are followed by two bytes high then low , indicating the length of marker-specific payload data that follows.
The length includes the two bytes for the length, but not the two bytes for the marker. Some markers are followed by entropy-coded data; the length of such a marker does not include the entropy-coded data. Note that consecutive 0xFF bytes are used as fill bytes for padding purposes, although this fill byte padding should only ever take place for markers immediately following entropy-coded scan data see JPEG specification section B.
Within the entropy-coded data, after any 0xFF byte, a 0x00 byte is inserted by the encoder before the next byte, so that there does not appear to be a marker where none is intended, preventing framing errors. Decoders must skip this 0x00 byte. Note however that entropy-coded data has a few markers of its own; specifically the Reset markers 0xD0 through 0xD7 , which are used to isolate independent chunks of entropy-coded data to allow parallel decoding, and encoders are free to insert these Reset markers at regular intervals although not all encoders do this.
Since several vendors might use the same APP n marker type, application-specific markers often begin with a standard or vendor name e.
At a restart marker, block-to-block predictor variables are reset, and the bitstream is synchronized to a byte boundary. Restart markers provide means for recovery after bitstream error, such as transmission over an unreliable network or file corruption. Since the runs of macroblocks between restart markers may be independently decoded, these runs may be decoded in parallel. The encoding process consists of several steps:. The decoding process reverses these steps, except the quantization because it is irreversible.
In the remainder of this section, the encoding and decoding processes are described in more detail. Many of the options in the JPEG standard are not commonly used, and as mentioned above, most image software uses the simpler JFIF format when creating a JPEG file, which among other things specifies the encoding method.
Here is a brief description of one of the more common methods of encoding when applied to an input that has 24 bits per pixel eight each of red, green, and blue. This particular option is a lossy data compression method. It has three components Y', C B and C R : the Y' component represents the brightness of a pixel, and the C B and C R components represent the chrominance split into blue and red components. This is basically the same color space as used by digital color television as well as digital video including video DVDs.
The compression is more efficient because the brightness information, which is more important to the eventual perceptual quality of the image, is confined to a single channel. This more closely corresponds to the perception of color in the human visual system. The color transformation also improves compression by statistical decorrelation. However, some JPEG implementations in "highest quality" mode do not apply this step and instead keep the color information in the RGB color model , [50] where the image is stored in separate channels for red, green and blue brightness components.
This results in less efficient compression, and would not likely be used when file size is especially important. Due to the densities of color- and brightness-sensitive receptors in the human eye, humans can see considerably more fine detail in the brightness of an image the Y' component than in the hue and color saturation of an image the Cb and Cr components.
Using this knowledge, encoders can be designed to compress images more efficiently. The ratios at which the downsampling is ordinarily done for JPEG images are no downsampling , reduction by a factor of 2 in the horizontal direction , or most commonly reduction by a factor of 2 in both the horizontal and vertical directions.
For the rest of the compression process, Y', Cb and Cr are processed separately and in a very similar manner. In video compression MCUs are called macroblocks. If the data for a channel does not represent an integer number of blocks then the encoder must fill the remaining area of the incomplete blocks with some form of dummy data.
Filling the edges with a fixed color for example, black can create ringing artifacts along the visible part of the border; repeating the edge pixels is a common technique that reduces but does not necessarily eliminate such artifacts, and more sophisticated border filling techniques can also be applied. This step reduces the dynamic range requirements in the DCT processing stage that follows. If we perform this transformation on our matrix above, we get the following rounded to the nearest two digits beyond the decimal point :.
Note the top-left corner entry with the rather large magnitude. This is the DC coefficient also called the constant component , which defines the basic hue for the entire block. The remaining 63 coefficients are the AC coefficients also called the alternating components. The quantization step to follow accentuates this effect while simultaneously reducing the overall size of the DCT coefficients, resulting in a signal that is easy to compress efficiently in the entropy stage.
This may force the codec to temporarily use bit numbers to hold these coefficients, doubling the size of the image representation at this point; these values are typically reduced back to 8-bit values by the quantization step. The temporary increase in size at this stage is not a performance concern for most JPEG implementations, since typically only a very small part of the image is stored in full DCT form at any given time during the image encoding or decoding process.
The human eye is good at seeing small differences in brightness over a relatively large area, but not so good at distinguishing the exact strength of a high frequency brightness variation. This allows one to greatly reduce the amount of information in the high frequency components.
This is done by simply dividing each component in the frequency domain by a constant for that component, and then rounding to the nearest integer.
This rounding operation is the only lossy operation in the whole process other than chroma subsampling if the DCT computation is performed with sufficiently high precision.
As a result of this, it is typically the case that many of the higher frequency components are rounded to zero, and many of the rest become small positive or negative numbers, which take many fewer bits to represent. The elements in the quantization matrix control the compression ratio, with larger values producing greater compression. Notice that most of the higher-frequency elements of the sub-block i.
Entropy coding is a special form of lossless data compression. It involves arranging the image components in a " zigzag " order employing run-length encoding RLE algorithm that groups similar frequencies together, inserting length coding zeros, and then using Huffman coding on what is left.
It has some basic editing functions like crop, color, and tone adjustment, too. File directories are displayed in the left-hand pane, allowing users to switch between folders and storage devices with the same ease you would expect from File Explorer or Finder. In that same pane it also includes Favorites and Categories Filter tabs to help maintain and search for the appropriate photos.
When saving though, it can export to approximately 70 different file formats. Keyboard shortcuts can be utilized to quickly rate images for categorization later. The MP version contains all the XNView Classic features plus a few more and is optimized for Windows, Mac, and Linux systems on both bit and bit operating systems — making it accessible to almost everyone with a computer.
Having the best photo organizing software is all well and good, but you need to use it in the right way to get the most from it. Here are three tips to help you get the most out of your photo organizer software.
Ideally, you'd all give each of our photos a distinct and unique name. But in practice, there just isn't enough time in the day, so it's best to come up with a clear and consistent naming convention to help you keep track of them.
It's basically a question of finding a system that works for you. For example, you might give all of the photos from a particular shoot the same name and date and then a number, such as Stonehenge, Stonehenge etc. However, if you don't have a good memory for when different shoots took place at the same location, you might want to add some context, such like Stonehenge-festival-sunrise, or Stonehenge-clothing-ad That might seem like a lot of typing, but most photo organizer software makes it easy to batch-name a group of images in this way.
However thoughtfully you group your photos in folders, there'll be times when you're searching for specific categories of image that don't fit in that folder structure. So it's a worthwhile time investment to add as many tags as you can. This will be enormously helpful in finding images in future. This process is quite similar adding hashtags on social media platforms like Instagram, or in a stock photo library.
The main difference is that you're adding tags that you, rather than others, would be likely to search for. Include everything from descriptive words 'nature', 'outdoors', 'snow' etc to those relating to the image's mood 'happy', 'gloomy', 'peaceful' to technical aspects 'bokeh', '50mm', 'macro'. Again, this sounds like a lot of work, but photo organizer software can help to automate this process.
Even if every one of your photos has a uniquely identifiable name, that's only the beginning of organizing them. It's also important to store your images in folders, and folders within those folders, so you can keep track of everything as time goes on. How exactly you divide your images up will depend on your own needs. For example, some people will be more interested in grouping shots by date, and others by style eg, portrait versus landscape, or high ISO versus low ISO.
There is no 'right' answer here, it's purely about what is going to work for you. Join now for unlimited access. For nearly two decades Sebastian's work has been published internationally. Sign in View Profile Sign out. Adobe Lightroom Classic. The best photo organizing software overall, though images must be imported first. Payment model: Annual subscription paid monthly.
Free trial: 7 days. Reasons to avoid - More costly than some. Apple Photos. Payment model: Free payment for upgraded storage. Reasons to avoid - Apple devices only. Google Photos. Reasons to avoid - Payment required for upgraded storage. Adobe Bridge. Specifications Compatible with: Windows 10 bit version or later, macOS v Payment model: Free or as part of Creative Cloud subscription.
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