Complexity management for video encoders
Proceedings of the tenth ACM international conference on Multimedia
Rate distortion and denoising of individual data using Kolmogorov complexity
IEEE Transactions on Information Theory
MPEG-21 standardization process: organization and rate distortion modeling for network adaptation
ICCOM'10 Proceedings of the 14th WSEAS international conference on Communications
Computation of the complexity of vector quantizers by affine modeling
Signal Processing
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Complexity distortion theory (CDT) is a mathematical framework providing a unifying perspective on media representation. The key component of this theory is the substitution of the decoder in Shannon's classical communication model with a universal Turing machine. Using this model, the mathematical framework for examining the efficiency of coding schemes is the algorithmic or Kolmogorov (1965) complexity. CDT extends this framework to include distortion by defining the complexity distortion function. We show that despite their different natures, CDT and rate distortion theory (RDT) predict asymptotically the same results, under stationary and ergodic assumptions. This closes the circle of representation models, from probabilistic models of information proposed by Shannon in information and rate distortion theories, to deterministic algorithmic models, proposed by Kolmogorov in Kolmogorov complexity theory and its extension to lossy source coding, CDT.