Critical Data Compression

TL;DR

This paper introduces a novel two-part data compression method that separates signals into lossless and lossy components, improving compression quality for multimedia and AI applications by encoding significant bits exactly and modeling residual noise.

Contribution

The paper presents a new two-part compression approach that combines lossless and lossy coding, enhancing multimedia compression and inference efficiency over traditional methods.

Findings

Better compression of images, audio, and video than standard lossy codecs.

Improved inference performance with signals having optimal redundancy.

Effective encoding of periodic and chaotic data types.

Abstract

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.