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  2. Smallpdf.com - Wikipedia

    en.wikipedia.org/wiki/Smallpdf.com

    Smallpdf is a Swiss online web-based PDF software, founded in 2013. [2] It offers free version with limited features to compress, convert and edit PDF documents. [3] And its paid version offers advanced features like OCR, compress, and more [4].

  3. File size - Wikipedia

    en.wikipedia.org/wiki/File_size

    Due to typical file system design, the amount of space allocated for a file is usually larger than the size of the file's data – resulting in a relatively small amount of storage space for each file, called slack space or internal fragmentation, that is not available for other files but is not used for data in the file to which it belongs.

  4. Comparison of e-book formats - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_e-book_formats

    Derived from PostScript, but without language features like loops, PDF adds support for features such as compression, passwords, semantic structures and DRM. Because PDF documents can easily be viewed and printed by users on a variety of computer platforms , they are very common on the Internet and in document management systems worldwide.

  5. PDF - Wikipedia

    en.wikipedia.org/wiki/PDF

    Rich Media PDF is a PDF file including interactive content that can be embedded or linked within the file. It can contain images, audio, video content, or buttons. It can contain images, audio, video content, or buttons.

  6. Data compression - Wikipedia

    en.wikipedia.org/wiki/Data_compression

    An alternative view can show compression algorithms implicitly map strings into implicit feature space vectors, and compression-based similarity measures compute similarity within these feature spaces. For each compressor C(.) we define an associated vector space ℵ, such that C(.) maps an input string x, corresponding to the vector norm ||~x||.

  7. Lossless compression - Wikipedia

    en.wikipedia.org/wiki/Lossless_compression

    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.

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