Abstract
With the increased use of image acquisition devices, including cameras and medical imaging instruments, the amount of information ready for long term storage is also growing. In this paper we give a detailed description of the state-of-the-art lossless compression software PAQ8PX applied to grayscale image compression. We propose a new online learning algorithm for predicting the probability of bits from a stream. We then proceed to integrate the algorithm into PAQ8PX’s image model. To verify the improvements, we test the new software on three public benchmarks. Experimental results show better scores on all of the test sets.
Highlights
Why is compression a difficult problem? In general, when it comes to predicting something, you need to understand the process behind the result
The application is implemented in the C++ programming language, since the PAQ8PX was already implemented in this language
This paper provides a description of the state-of-the-art compression program PAQ8PX from the point of view of grayscale image compression
Summary
Why is compression a difficult problem? In general, when it comes to predicting something, you need to understand the process behind the result. There are two important operations that require improvement: the storage of images, be it for long or short term (archiving), and the transmission of images via networks When it comes to quality, lossy methods need to keep the quality of the image high to prevent mispronounced diagnostics. PAQ is a series of experimental lossless data compression software aiming at the best compression ratio for a wide range of file types without a focus on using few computing resources or keeping backward version compatibility. It was started by Matt Mahoney and later developed by more than 20 developers in different branches of compression. A description of the PAQ series of compressors from the perspective of machine learning is available at [18]
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