Data compression project

TODO deadline 25.2.2019

• Tweak Z-Order reordering
  • Values, which are stored in more than one byte, must stay together.
  • Pixels, which contains more channels, can be separeted in final z order. (Try both separeted and not-separated)
    • Right now, when we reorder bytes we move whole pixel (all channels), but when pixel's channels will be separated we will move just one channel.
• Feed binary data to existing compressors and produce results (tables, graphs)
  • Try both data in their order and in Z-order
  • Compressors to try:
    • gZip (zLib, huffman deflate combination)
    • Bzip2
    • LZMA (7-zip)
    • Add level CLI option to specify compression level for above algorithms
      • Use different Compression strategy
    • Add CSV writer, which will write test results to file and test function taking folder with test files to parse.
    • B3D cuda library
• Look at Image difference
  • Negative values can be mapped to odd/even numbers. But the difference must be saved in more than one byte.
  • Save difference in different type than byte (short, uint, ulong)
• Compose results into paper.

CZI parser TODO list

This is list of things, which have to be done first:

• Parse SubBlockDirectory (there will be collection of DirectoryEntryDV's)
  • Exact copy of DirectoryEntryDV will be located in the referenced SubBlock
  • Parse dimensions entries
• Parse IEEE 4 / 8 byte float.
  • Parsing is done via memcpy call. Other alternative is using union, but double parsing wass't working with it. Later we can take a look on this and maybe improve the conversion and get rid of the copy.
• Parse SubBlock
  • Parse image data to proper pixel type. Do we really want to do that? We really care only about bytes. Parsing image data into some PixelType Matrix may be good, only if we want to do some operation on the image. This is moved to later section then.
• Parse / extract image data from SubBlock
  • We are aware of position and size of the data in CZI file. With that two informations we can extract the data easily.
• Parse important informations from XML metadata (e.g. BitsPerPixel and compare with value from parsed binary data).
• Parse image values into matrices. Matrix<Gray8> etc.
• Support multi-file situations
  • Obtain multi-file CZI files. Currently we don't have any multi-files, so we can't really parse them. Secondary files should have different GUID than master file, also filepart should be different from 0. Files we have right now have (0) in their name, but theirs filepart is 0 and GUID of master file isn't set correctly.
  • One master file and more secondary files files (our current CziFile class kinda support that situations, so keep going that way)

Later on, we can extend our program to handle more things from the file, like:

• Parse metadata according to XML schemas
• Take a look on binary reader, can it be fastened up?
• Parse segments from memory buffer rather than from file stream. (Disk bottleneck)
• Parse image data to PixelType matrix type.

Compression of images

• I tested FLIF compression on ~200 MB file. Compression ratio was good but the speed on the other hand was really bad. Decompression wasn't better, it was slow too.
  • Even lossy compression was slow. This compression is probably only good for small images.
• Next step is to test B3D compression.
• Pure LZW creates dictionary as it goes. Can create entries which aren't found in data.
• LZ77 is better variant.
• Look at Huffman encoding, then maybe Arithmetic and in a spare time install nVidia drivers to test ot B3D library.

Space filling (Peano) curves

• Wikipedia
• Find out what is this about
• Look specifinally at Z-order curve and Hilbert curve

Image difference

• Find difference between included images.
• Plot those differences and find out if certain images aren't same, just positioned little bit differently
• Pattern matching - Find if there are some patterns in image sets.