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Related Experiment Video

Updated: Jul 7, 2026

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Subband coding systems incorporating quantizer models.

¹Signal Process. Res. Dept., AT&T Bell Labs., Murray Hill, NJ.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 1, 1995

Summary

This study introduces a novel method to reduce quantization errors in subband systems. By canceling signal-dependent errors, the approach minimizes distortion, leading to improved image reconstruction quality.

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Last Updated: Jul 7, 2026

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Area of Science:

Digital Signal Processing
Image Processing
Information Theory

Background:

Quantization introduces errors in subband systems, degrading signal quality.
Existing methods often result in signal-dependent errors, limiting performance.
The Lloyd-Max quantizer's "gain plus additive noise" model provides a framework for error analysis.

Purpose of the Study:

To propose a new method for mitigating quantization effects in subband systems.
To demonstrate the cancellation of signal-dependent errors through filter design.
To extend the method to multidimensional signals and Quadrature Mirror Filter (QMF) systems.

Main Methods:

Utilizing the "gain plus additive noise" linear model for Lloyd-Max quantizers.
Designing synthesis filters to cancel signal-dependent quantization errors.
Applying noise removal techniques to reduce remaining random errors.
Extending the methodology to multidimensional signals and arbitrary sampling lattices.
Validating the approach through experimental image processing.

Main Results:

Signal-dependent errors are effectively canceled, leaving only random, uncorrelated noise.
A trade-off exists between random error and signal-dependent error, with comparable variances.
Noise removal techniques can further reduce the residual random error.
The method shows improved performance even when subbands are discarded in realistic coding schemes.
Experimental results on images demonstrate reduced error correlation and visually similar reconstructions.

Conclusions:

The proposed method significantly reduces quantization-induced errors in subband systems.
Cancellation of signal-dependent errors leads to more robust signal reconstruction.
The technique offers a viable alternative to conventional subband systems, especially for image coding.
Further improvements are possible through post-processing techniques like noise removal.