Document Conversion

The term round-trip is commonly used in document conversion particularly involving markup languages such as XML and SGML. A successful round-trip consists of converting a document in format A (docA) to one in format B (docB) and then back again to format A (docA'). If docA and docA' are identical then there has been no information loss and the round-trip has been successful. More generally it means converting from any data representation and back again, including from one data structure to another.

When a document in one format is converted to another there is likely to be information loss. For example, suppose an HTML document is saved as plain text (.txt). Then all the markup (structure, formatting, superscripts, …) will be lost. Compound documents will frequently lose information on images and other embedded objects. If the text file is converted back to the original format, information will necessarily be missing.

A similar effect happens with image formats. Some formats such as JPEG achieve compression through small amount of information loss. If a lossless file, such as a BMP or PNG file, is converted to JPEG and back again then the result will be different from the original (although it may be visually very similar).

Just because the initial and final documents are not bitwise identical does not mean there is information loss. Some formats have undefined fields, or fields where the contents have no impact on the result.

Markup languages

Markup languages such as XML can, in principle, hold any information and so the process docA ? docX ? docA' could be designed to avoid information loss. It is now common to convert legacy formats to XML formats because they have greater interoperability and a wider set of available tools. Thus it is possible to convert Word documents to an XML format and reimport them.

The XML document should contain identical information to the legacy format. An important condition is that the roundtrip (legacy ? XML ? legacy') should result in effectively identical documents. Because some document structures allow some flexibility in content order, whitespace, case-sensitivity, etc. it is useful to have a means of canonicalizing the legacy format. The full roundtrip may then be:

legacy ? canonicalLegacy ? XML ? legacy' ? canonicalLegacy'

If canonicalLegacy = canonicalLegacy' then the roundtrip has been successful.

Data Conversion

Data conversion is the conversion of one form of computer data to another--the changing of bits from being in one format to a different one, usually for the purpose of application interoperability or of capability of using new features. At the simplest level, data conversion can be exemplified by conversion of a text file from one character encoding to another. More complex conversions are those of office file formats, and conversions of image and audio file formats are an endeavor that is beyond the ken of ordinary computer users.

Before any data conversion is carried out, the user or application programmer should keep a few basics of computing and information theory in mind. These include:

Information can easily be discarded by the computer, but adding information takes effort.
The computer can add information only in a rule-based fashion; most users want additions of information that can only be accomplished by humans.
Upsampling the data or converting to a more feature-rich format does not add information; it merely makes room for that addition, which usually a human must do.

For example, a truecolor image can easily be converted to grayscale, while the opposite conversion is a painstaking process. Converting a Unix text file to a Microsoft (DOS/Windows) text file involves adding information, namely a CR (hexadecimal 0D) byte before each LF (0A) byte, but that addition is easily done with a computer, since it is rule-based; whereas the addition of color information to a grayscale image cannot be done programmatically, since only a human knows which colors are needed for each section of the picture--there are no rules that can be used to automate that process. Converting a 24-bit PNG to a 48-bit one does not add information to it, it only pads existing RGB pixel values with zeroes, so that a pixel with a value of FF C3 56, for example, becomes FF00 C300 5600. The conversion makes it possible to change a pixel to have a value of, for instance, FF80 C340 56A0, but the conversion itself does not do that, only further manipulation of the image can. Converting an image or audio file in a lossy format (like JPEG or Vorbis) to a lossless (like PNG or FLAC) or uncompressed (like BMP or WAV) format only wastes space, since the same image with its loss of original information (the artifacts of lossy compression) becomes the target. A JPEG image can never be restored to the quality of the original lossless image from which it was made, no matter how much the user tries the "JPEG Artifact Removal" feature of his or her image manipulation program.

Because of these realities of computing and information theory, data conversion is more often than not a complex and error-prone process that requires the help of experts. It is safe to say that only the success of artificial intelligence could put data conversion companies out of a job.

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