
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…
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Taxonomy
TopicsComputability, Logic, AI Algorithms · Algorithms and Data Compression · Numerical Methods and Algorithms
