Arithmetic Coding

By Mathias Winther. 1979

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Citations in the corpus

Wavenet Based Low Rate Speech Coding (2017, methodology): “Using techniques such as arithmetic coding [24, 25] can provide near-optimal coding for an entire sequence of indices.”
PRISM: A Video Coding Paradigm With Motion Estimation at the Decoder (2007, unknown): “Huffman coding [29] or arithmetic coding [35].”
Deep Probabilistic Video Compression (2018, methodology): “This allows both to transform the video into a low dimensional latent representation and to then use the jointly learned predictive model to remove the remaining redundancy in the latents by entropy coding them to a short binary representation (Huffman, 1952; Langdon, 1984).”
A Machine Learning Perspective on Predictive Coding with PAQ8 (2012, methodology): “The second is to encode these probability distributions into a file using a coding scheme such as arithmetic coding [8] or Huffman coding [9].”
Information Theory and Statistics: A Tutorial (2004, methodology): “Elias, was developed to a powerful data compression technique primarily by Rissanen, see [34], [39].”
Integer Discrete Flows and Lossless Compression (2019, background): “Shannon’s source coding theorem [28] states that the optimal code length for a symbol x is − log p(x), and the minimum expected code length is lower-bounded by the entropy: Ex∼D [|c(x)”
Robust Low Rate Speech Coding Based on Cloned Networks and Wavenet (2020, background): “It is well-known that entropy-coders such as arithmetic coding [29] can encode the quantization indices arbitrarily close to their entropy.”
Introduction to Data Compression (2005, background): “Finally, several papers appeared that provided practical arithmetic coding algorithms, the most well known of which is the paper by Rissanen and Langdon [43].”
A Comprehensive Study of the Past, Present, and Future of Data Deduplication (2016, background): “, the aforementioned entropy value), arithmetic coding [41], first proposed by Elias in the 1960s [42], encodes the entire message into several decimals for a higher compression ratio.”
Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model (1997, background): “To address this problem, consider the operation of an arithmetic decoder [5] [6] [7].”
Towards Backward-Compatible Continual Learning of Image Compression (2024, background): “The pri mary reason lies in entropy coding: range-based entropy coding algorithms [16, 46], which are commonly used in modern neural compressors, are known to be sensitive to the probability distribution of the encoded symbols.”