Information-theoretic approach to optimal quantization

Maximo Lorenzo; Sandor Z. Der; J. Russ Moulton Jr.

doi:10.1117/12.45736

1 July 1991 Information-theoretic approach to optimal quantization

Maximo Lorenzo, Sandor Z. Der, J. Russ Moulton Jr.

Proceedings Volume 1483, Signal and Image Processing Systems Performance Evaluation, Simulation, and Modeling; (1991) https://doi.org/10.1117/12.45736
Event: Orlando '91, 1991, Orlando, FL, United States

Abstract

The authors offer transinformation maximization as the criterion for optimal signal quantization for most applications in lieu of the more conventional criteria, such as mean- square-error minimization first proposed by Max. Optimal quantization for signal transinformation (which reduces to entropy in the noise-free case) using a uniform digitizer and a companding gain function is considered, and specifically applied to the image acquisition problem for ATR (Automatic Target Recognizer) processing. Both pre- and post-gain noisy channels are examined under linear sensor gain (with extension to non-linear gains); maximum achievable entropy, transinformation, SNR, and minimum mean square error are computed for several typical input distributions. The authors establish the following arguments supporting Maximum Transinformation Acquisition--MTA (noise)--and Maximum Entropy Acquisition-- MEA (no-noise): (1) MTA 'matches' the sensor/digitizer channel to the input signal in an information-theoretic sense, preserving as much analog information as possible. (2) MTA can be closely approximated with a less computationally intensive algorithm than actual transinformation. (3) Net information content is the crucial quantity governing a priori detection and recognition in cluttered environments. (4) MEA provides the best overall pixel intensity spread throughout the image, maximizing the probability of pixel differences where analog intensities differ; MTA provides very near optimal SNR and noise-free pixel contrasts. (5) MTA provides a standard, repeatable, and globally optimal acquisition method for extracting information for ATRs in the absence of a priori scene knowledge; MTA can also be applied locally to maximize information content within a region of interest.

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Maximo Lorenzo, Sandor Z. Der, and J. Russ Moulton Jr. "Information-theoretic approach to optimal quantization", Proc. SPIE 1483, Signal and Image Processing Systems Performance Evaluation, Simulation, and Modeling, (1 July 1991); https://doi.org/10.1117/12.45736

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KEYWORDS

Interference (communication)

Signal to noise ratio

Quantization

Image processing

Analog electronics

Signal processing

Sensors

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