Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Adapted Total Variation for Artifact Free Decompression of JPEG Images
Journal of Mathematical Imaging and Vision
Constrained and SNR-Based Solutions for TV-Hilbert Space Image Denoising
Journal of Mathematical Imaging and Vision
Multiple description coding with redundant expansions and application to image communications
Journal on Image and Video Processing
Reconstructing videos from multiple compressed copies
IEEE Transactions on Circuits and Systems for Video Technology
Asymptotic analysis of multiple description quantizers
IEEE Transactions on Information Theory
Multiple-description vector quantization with lattice codebooks: design and analysis
IEEE Transactions on Information Theory
Multiple description vector quantization with a coarse lattice
IEEE Transactions on Information Theory
Design of multiple description scalar quantizers
IEEE Transactions on Information Theory
The digital TV filter and nonlinear denoising
IEEE Transactions on Image Processing
Multiple description coding using pairwise correlating transforms
IEEE Transactions on Image Processing
An enhanced two-stage multiple description video coder with drift reduction
IEEE Transactions on Circuits and Systems for Video Technology
Efficient Multiple-Description Image Coding Using Directional Lifting-Based Transform
IEEE Transactions on Circuits and Systems for Video Technology
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This paper studies how to reconstruct pictures with the best possible quality upon receiving more than one description at the decoder side of a multiple description coding (MDC) system, assuming that an MDC encoder has been fixed at the encoder side to generate multiple descriptions. To this end, we formulate the problem into a total variation (TV) regularized optimization in which all received descriptions are regarded as targets to form multiple fidelity terms. Two solutions are then developed. First, we solve a standard Lagrange-type optimization involving multiple Lagrange multipliers, and this approach is applicable to any MDC encoder. Second, when multiple quantizers with different step-sizes or dead-zones are used to generate individual descriptions, we make use of the intersection of the overlapped quantization intervals (in the transform domain) in all received descriptions. Both solutions are demonstrated to offer a quality gain (subjective as well as objective) over what can be achieved in the existing methods. In particular, the second approach is found to offer the best gain consistently when a large number of descriptions are needed.