A generalized Gaussian entropy model with dynamically adjusted likelihood intervals reduces bitrate by 6 to 11 percent across three point-cloud attribute compression baselines.
Introduction to Arithmetic Coding -- Theory and Practice
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abstract
This introduction to arithmetic coding is divided in two parts. The first explains how and why arithmetic coding works. We start presenting it in very general terms, so that its simplicity is not lost under layers of implementation details. Next, we show some of its basic properties, which are later used in the computational techniques required for a practical implementation. In the second part, we cover the practical implementation aspects, including arithmetic operations with low precision, the subdivision of coding and modeling, and the realization of adaptive encoders. We also analyze the arithmetic coding computational complexity, and techniques to reduce it.
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Generalized Gaussian Entropy Model for Point Cloud Attribute Compression with Dynamic Likelihood Intervals
A generalized Gaussian entropy model with dynamically adjusted likelihood intervals reduces bitrate by 6 to 11 percent across three point-cloud attribute compression baselines.