A scalable multiple description scalar quantizer (SMDSQ) is a quantization based framework used for scalable multiple description coding (SMDC). In this paper, we introduce a novel generalization of the Lloyd-Max algorithm to realize locally optimal SMDSQs. Both level-constrained and entropy-constrained cases are considered. For both cases, locally optimal solutions are realized by iterative execution of the centroid and the modified nearest-neighbor conditions. Experimental results confirm that, for a zero-mean unit-variance Gaussian source, the optimization algorithm enables a significant reduction in distortion for the level-constrained case. Moreover, relatively lesser but still significant distortion-rate (D-R) gains are viable for the entropy-constrained case. It is shown that, for a packetized transmission of Gaussian as well as wavelet-decomposed images, the obtained optimization gains translate into an average improvement in the decoder's signal-to-noise-ratio (SNR) for a wide range of packet loss rates.
Satti, S, Deligiannis, N, Munteanu, A, Schelkens, P & Cornelis, J 2012, An Optimization Algorithm for Scalable Multiple Description Scalar Quantizers. in 2012 International Symposium on Information Theory and its Applications (ISITA2012). 2012 International Symposium on Information Theory and its Applications (ISITA2012), Honolulu, United States, 28/10/12.
Satti, S., Deligiannis, N., Munteanu, A., Schelkens, P., & Cornelis, J. (2012). An Optimization Algorithm for Scalable Multiple Description Scalar Quantizers. In 2012 International Symposium on Information Theory and its Applications (ISITA2012)
@inproceedings{e9620237dedc49e4ab4df684bec6e0bf,
title = "An Optimization Algorithm for Scalable Multiple Description Scalar Quantizers",
abstract = "A scalable multiple description scalar quantizer (SMDSQ) is a quantization based framework used for scalable multiple description coding (SMDC). In this paper, we introduce a novel generalization of the Lloyd-Max algorithm to realize locally optimal SMDSQs. Both level-constrained and entropy-constrained cases are considered. For both cases, locally optimal solutions are realized by iterative execution of the centroid and the modified nearest-neighbor conditions. Experimental results confirm that, for a zero-mean unit-variance Gaussian source, the optimization algorithm enables a significant reduction in distortion for the level-constrained case. Moreover, relatively lesser but still significant distortion-rate (D-R) gains are viable for the entropy-constrained case. It is shown that, for a packetized transmission of Gaussian as well as wavelet-decomposed images, the obtained optimization gains translate into an average improvement in the decoder's signal-to-noise-ratio (SNR) for a wide range of packet loss rates.",
keywords = "Multiple description coding, scalable multiple description quantizer, Quantizer optimization",
author = "Shahid Satti and Nikolaos Deligiannis and Adrian Munteanu and Peter Schelkens and Jan Cornelis",
year = "2012",
month = oct,
day = "28",
language = "English",
booktitle = "2012 International Symposium on Information Theory and its Applications (ISITA2012)",
note = "2012 International Symposium on Information Theory and its Applications (ISITA2012) ; Conference date: 28-10-2012 Through 31-10-2012",
}