Improving Lossless Image Compression with Contextual Memory
by
Alexandru Dorobanțiu *
and Remus Brad
Alexandru Dorobanțiu * and Remus Brad
Computer Science Department, Faculty of Engineering, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(13), 2681; https://doi.org/10.3390/app9132681
Received: 23 May 2019 / Revised: 26 June 2019 / Accepted: 28 June 2019 / Published: 30 June 2019
(This article belongs to the Special Issue Advanced Ultrafast Imaging)
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.
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Keywords:
lossless; image compression; ensemble learning; contextual information; probabilistic method; geometric weighting
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Dorobanțiu, A.; Brad, R. Improving Lossless Image Compression with Contextual Memory. Appl. Sci. 2019, 9, 2681.
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