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Image compression is a type of data compression applied to digital images, to reduce their cost for storage or transmission. Algorithms may take advantage of visual perception and the statistical properties of image data to provide superior results compared with generic data compression methods which are used for other digital data.
The TIFF (Tag Image File Format) format is a flexible format usually using either the TIFF or TIF filename extension. The tag structure was designed to be easily extendible, and many vendors have introduced proprietary special-purpose tags – with the result that no one reader handles every flavor of TIFF file.
The TIFF/EP standard does however have a few notable differences from the Exif standards: All tags used by TIFF/EP which are defined by the Exif standard to reside within the Exif Sub-IFD now reside directly under the first (main) TIFF IFD. The Exif IFD Sub-IFD, Interoperability Sub-IFD and the MakerNote Tag have all been omitted.
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The Exif tag structure is borrowed from TIFF files. On several image specific properties, there is a large overlap between the tags defined in the TIFF, Exif, TIFF/EP, and DCF standards. For descriptive metadata, there is an overlap between Exif, IPTC Information Interchange Model and XMP info, which also can be embedded in a JPEG file.
TIFF Revision 5.0: Specification for revision 5.0, in HTML (warning: for historical purposes only, the TIFF 6.0 spec contains the full 5.0 revision) TIFF Revision 6.0: Specification for revision 6.0, in PDF (warning: there is an outdated and flawed section (jpeg compression), corrected in supplements, and there are additions to this PDF too ...
Defines a basic framework for storing raw and processed images in TIFF. Is the basis of many other RAW formats: for example, Nikon NEF is essentially NEF, complete with a tag which identifies the version of TIFF/EP they are based on. [20] Digital Negative (DNG) 2004: Yes, from Adobe: Extension of TIFF/EP, adding information on camera ...
Most lossless compression programs do two things in sequence: the first step generates a statistical model for the input data, and the second step uses this model to map input data to bit sequences in such a way that "probable" (i.e. frequently encountered) data will produce shorter output than "improbable" data.