  A new approach to data compression is developed and applied to multimedia
content. This method separates messages into components suitable for both
lossless coding and 'lossy' or statistical coding techniques, compressing
complex objects by separately encoding signals and noise. This is demonstrated
by compressing the most significant bits of data exactly, since they are
typically redundant and compressible, and either fitting a maximally likely
noise function to the residual bits or compressing them using lossy methods.
Upon decompression, the significant bits are decoded and added to a noise
function, whether sampled from a noise model or decompressed from a lossy code.
This results in compressed data similar to the original. For many test images,
a two-part image code using JPEG2000 for lossy coding and PAQ8l for lossless
coding produces less mean-squared error than an equal length of JPEG2000.
Computer-generated images typically compress better using this method than
through direct lossy coding, as do many black and white photographs and most
color photographs at sufficiently high quality levels. Examples applying the
method to audio and video coding are also demonstrated. Since two-part codes
are efficient for both periodic and chaotic data, concatenations of roughly
similar objects may be encoded efficiently, which leads to improved inference.
Applications to artificial intelligence are demonstrated, showing that signals
using an economical lossless code have a critical level of redundancy which
leads to better description-based inference than signals which encode either
insufficient data or too much detail.
