Universal modeling and coding

By Jorma Rissanen, G. Langdon. 1981

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Citations in the corpus

End-to-end Optimized Image Compression (2016, background): “These may be signiﬁcantly reduced through the use of joint local nonlinear gain control operations (Schwartz and Simoncelli, 2001; Lyu, 2010; Sinz and Bethge, 2013), inspired by models of visual neurons (Heeger, 1992; Carandini and Heeger, 2012).”
High Fidelity Neural Audio Compression (2022, methodology): “We use a range based arithmetic coder (Pasco, 1976; Rissanen & Langdon, 1981) in order to leverage the estimated probabilities given by the language model.”
Variational image compression with a scale hyperprior (2018, methodology): “Because ˆ y is discrete-valued, it can be losslessly compressed using entropy coding techniques such as arithmetic coding (Rissanen and Langdon, 1981) and transmitted as a sequence of bits.”
Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules (2020, methodology): “If a probability model pŷ(ŷ) is given, entropy coding techniques, such as arithmetic coding [28], can losslessly compress the quantized codes.”
Model Compression by Entropy Penalized Reparameterization (2019, methodology): “Then, a Shannon-style entropy coding method such as arithmetic coding (Rissanen and Langdon, 1981) is applied to the discrete values, bringing them into a binary representation which can be easily stored or transmitted.”
Using Free Energy Principle For Blind Image Quality Assessment (2015, background): “Moreover, a more complex model with a large number of parameters has a higher model cost in the theory of model selection [46], and thus more difficult to estimate from observations.”
Checkerboard Context Model for Efficient Learned Image Compression (2021, background): “By estimating the probability distribution of latent representations, such models can minimize the entropy of these representations to be compressed, which directly correlates to the final code length using arithmetic encoding [32] or range encoding [24], and enable a differentiable form of rate-dis”
Prediction, learning, and games (2006, methodology): “The connection of sequential probability assignment and data compression with arithmetic coding was first revealed by Rissanen [236] and Rissanen and Langdon [243].”
Joint Autoregressive and Hierarchical Priors for Learned Image Compression (2018, methodology): “Finally, the entropy model is used in conjunction with standard entropy coding algorithms such as arithmetic, range, or Huffman coding [15]–[17] to generate a compressed bitstream.”
The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS (2000, methodology): “The conceptual separation between the modeling and coding operations [9] was made possible by the invention of the arith-”
The Minimum Description Length Principle in Coding and Modeling (1998, background): “A particularly convenient way to do it is by an Arithmetic Code, see, e.g., Rissanen and Langdon [39], which is capable of encoding the individual symbols, even if they are binary, without the need to block them as required in the conventional Huffman codes.”
Efficient universal lossless data compression algorithms based on a greedy sequential grammar transform .2. With context models (2000, background): “2 Maintaining a finite set of contexts, Rissanen and Langdon [ 15 ] extended a unifilar model to a general recursive model.”