High-resolution quantization theory and the vector quantizer advantage

Lookabaugh, T.; Gray, Robert M.

doi:10.1109/18.42217

Cited by 175 publications

(103 citation statements)

References 35 publications

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“…Moreover, unlike optimum entropy-constrained vector quantization (ECVQ) [11,22], the entropy-coding gain in the mismatched case does not vanish even in the limit of large vector dimension. For a rather trivial example, note that the un-coded rate of an unbounded lattice quantizer is infinite, but it becomes finite after entropy-coding if the source has finite variance.…”

Section: Mismatched Quantization and Compressionmentioning

confidence: 99%

Mismatched codebooks and the role of entropy-coding in lossy data compression

Kontoyiannis

Zamir

2003

IEEE International Symposium on Information Theory, 2003. Proceedings.

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-We introduce a universal quantization scheme based on random coding, and we analyze its performance. This scheme consists of a source-independent random codebook (typically mismatched to the source distribution), followed by optimal entropy-coding that is matched to the quantized codeword distribution. A single-letter formula is derived for the rate achieved by this scheme at a given distortion, in the limit of large codebook dimension. The rate reduction due to entropycoding is quantified, and it is shown that it can be arbitrarily large. In the special case of "almost uniform" codebooks (e.g., an i.i.d. Gaussian codebook with large variance) and difference distortion measures, a novel connection is drawn between the compression achieved by the present scheme and the performance of "universal" entropy-coded dithered lattice quantizers. This connection generalizes the "half-a-bit" bound on the redundancy of dithered lattice quantizers. Moreover, it demonstrates a strong notion of universality where a single "almost uniform" codebook is near-optimal for any source and any difference distortion measure. The proofs are based on the fact that the limiting empirical distribution of the first matching codeword in a random codebook can be precisely identified. This is done using elaborate large deviations techniques, that allow the derivation of a new "almost sure" version of the conditional limit theorem.

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Section: Mismatched Quantization and Compressionmentioning

confidence: 99%

Mismatched codebooks and the role of entropy-coding in lossy data compression

Kontoyiannis

Zamir

2003

IEEE International Symposium on Information Theory, 2003. Proceedings.

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“…It is known that direct vector quantization leads to the highest coding efficiency due to its memory, space-filling and shape advantages [4]. A drawback of vector quantization is that it requires a large storage memory especially in the case of multiple-description quantization.…”

Section: Introductionmentioning

confidence: 99%

Rate distribution between model and signal for multiple descriptions

Klejsa

Kleijn

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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We consider the rate allocation problem for multiple-description quantization of the signal described by an adaptive model with a fixed structure. The source modeling in coding generally results in a two-stage description of the data, where one of the stages describes the model parameters, and the other describes the signal. Such a setup implies the existence of a trade-off between the rate spent on the parameters and the rate spent on the signal. We optimize this trade-off analytically for the multiple-description case using a method inspired by Minimum Description Length principle. We also provide an algorithm for optimizing the rate allocation between the components of the model-based multiple description coder. Finally we experimentally confirm our results. Our method facilitates the rate-adaptive multiple-description coding.

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“…The definition of the performance gain parallels a similar development by Lookabaugh and Gray [12]. Let us define two measures of conditional entropy advantage (CEA): first, the ratio between per-letter distortions obtained by an entropyconstrained vector quantizer and a conditional entropy-constrained vector quantizer, operating at the same rate and block size L,…”

Section: Theoretical Performancesmentioning

confidence: 99%