$M$-Channel Multiple Description Coding With Two-Rate Coding and Staggered Quantization

Samarawickrama, U.; Liang, Jie; Tian, Chao

doi:10.1109/tcsvt.2010.2045820

Cited by 19 publications

(5 citation statements)

References 27 publications

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“…Note that (49)-(50) have the same structures as ( 14)- (15). By proper variable replacement, the equivalent form of (49) is J IGC ( X), where X is as given by (23).…”

Section: Discussionmentioning

confidence: 99%

“…The proposed index assignment scheme utilizes a so-called staggered gain, which stems from a construction of translated lattices. Several practical multiple description schemes attempt to utilize this gain in a heuristic manner for a two-description scalar case [13], a two-description vector case [14] and K-description case with a two-stage coding [15]. In our work the index assignment exploits the staggered gain for an arbitrary number of descriptions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Index Assignment for Multiple Description Scalar Quantization With Translated Lattice Codebooks

Zhang

Klejsa

Kleijn

2012

IEEE Trans. Signal Process.

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We provide a method for designing an optimal index assignment for scalar K-description coding. The method stems from a construction of translated scalar lattices, which provides a performance advantage by exploiting a so-called staggered gain. Interestingly, generation of the optimal index assignment is based on a lattice in K − 1 dimensional space. The use of the K − 1 dimensional lattice facilitates analytic insight into the performance and eliminates the need for a greedy optimization of the index assignment. It is shown that that the optimal index assignment is not unique. This is illustrated for the two-description case, where a periodic index assignment is selected from possible optimal assignments and described in detail. The new index assignment is applied to design of a K-description quantizer, which is found to outperform a reference K-description quantizer at high rates. The performance advantage due to the staggered gain increases with increasing redundancy among the descriptions. Index TermsMultiple description quantization, Index assignment.

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“…Note that (49)-(50) have the same structures as ( 14)- (15). By proper variable replacement, the equivalent form of (49) is J IGC ( X), where X is as given by (23).…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Index Assignment for Multiple Description Scalar Quantization With Translated Lattice Codebooks

Zhang

Klejsa

Kleijn

2012

IEEE Trans. Signal Process.

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“…It can be seen that (10) and (11) correspond to projections over the ℓ 2 balls of radii ε B and ε b centered at h and l respectively. Similarly, (12) and (13) correspond to projections over the ℓ ∞ balls of diameters ∆ B and ∆ b centered at h and l respectively. The results of the projections are (5), (6), (7) and (8).…”

Section: Alternating Directions Minimizationmentioning

confidence: 99%

Graded Quantization for Multiple Description Coding of Compressive Measurements

Valsesia

Coluccia

Magli

2015

IEEE Trans. Commun.

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Compressed sensing (CS) is an emerging paradigm for acquisition of compressed representations of a sparse signal. Its low complexity is appealing for resource-constrained scenarios like sensor networks. However, such scenarios are often coupled with unreliable communication channels and providing robust transmission of the acquired data to a receiver is an issue. Multiple description coding (MDC) effectively combats channel losses for systems without feedback, thus raising the interest in developing MDC methods explicitly designed for the CS framework, and exploiting its properties. We propose a method called Graded Quantization (CS-GQ) that leverages the democratic property of compressive measurements to effectively implement MDC, and we provide methods to optimize its performance. A novel decoding algorithm based on the alternating directions method of multipliers is derived to reconstruct signals from a limited number of received descriptions. Simulations are performed to assess the performance of CS-GQ against other methods in presence of packet losses. The proposed method is successful at providing robust coding of CS measurements and outperforms other schemes for the considered test metrics.

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“…The joint optimization of all components is formulated [30]. The framework in [32] is similar to that of the PTSQ [23]. The key improvement of this method is that the coarse quantizers in different descriptions of this method are designed to be mutually refinable, in order to reduce the distortion when some descriptions are lost.…”

Section: Organizationmentioning

confidence: 99%

Network-adaptive prediction-compensated polyphase multiple description image coding and contrast-guided image interpolation

Wei¹

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There are many people who deserve more than just an acknowledgement on this page. First, I would like to thank my supervisor, Professor Kai-Kuang Ma, who advised this thesis. It is a privilege to learn from him for five years where he gave me much freedom in doing research. He is very supportive and patient. Most importantly, he not only directed my research work, but also gave me precious suggestions and instructions on the research attitude and effective communications. These valuable experiences will greatly influence my future career and life. I would also like to thank my earlier teachers without whose support I might never come to this field for a PhD degree, in particular the advisor of my master degree, Professor Canhui Cai in Huaqiao University. They inspired my interests in thinking and learning. I would like to thank my labmates, Tian Jing, Zeng Huanqiang, Wang Chen and Ong Hui Ting for their valuable advices and discussions on my research. I would also like to thank all of my friends who have made my time at Nanyang Technological University enjoyable. Finally, I would like to thank my wife, my daughter, and my parents for their unselfish support all the time. Without their love, this work would be impossible. i Contents Acknowledgments i List of Figures v List of Tables xii Summary xiv the interpolation process through the developed contrast-guided decision map (CD-M), which is a binary map and generated by applying the diffusion process to the directional variation field. The generated CDM is used to guide the interpolation process, and the local contrasts are better preserved compared with that of using the edge map (or, edge-guided decision map) for guiding the image interpolation process. Simulation results have clearly shown that the proposed contrast-guided interpolation method significantly outperforms other state-of-the-art image interpolation methods with much reduced computational complexity.

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$M$-Channel Multiple Description Coding With Two-Rate Coding and Staggered Quantization

Cited by 19 publications

References 27 publications

Optimal Index Assignment for Multiple Description Scalar Quantization With Translated Lattice Codebooks

Optimal Index Assignment for Multiple Description Scalar Quantization With Translated Lattice Codebooks

Graded Quantization for Multiple Description Coding of Compressive Measurements

Network-adaptive prediction-compensated polyphase multiple description image coding and contrast-guided image interpolation

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