A Non-Local Structure Tensor Based Approach for Multicomponent Image Recovery Problems

Non-Local Total Variation (NLTV) has emerged as a useful tool in variational methods for image recovery problems. In this paper, we extend the NLTV-based regularization to multicomponent images by taking advantage of the Structure Tensor (ST) resulting from the gradient of a multicomponent image. The proposed approach allows us to penalize the non-local variations, jointly for the different components, through various $\ell_{1,p}$ matrix norms with $p \ge 1$. To facilitate the choice of the hyper-parameters, we adopt a constrained convex optimization approach in which we minimize the data fidelity term subject to a constraint involving the ST-NLTV regularization. The resulting convex optimization problem is solved with a novel epigraphical projection method. This formulation can be efficiently implemented thanks to the flexibility offered by recent primal-dual proximal algorithms. Experiments are carried out for multispectral and hyperspectral images. The results demonstrate the interest of introducing a non-local structure tensor regularization and show that the proposed approach leads to significant improvements in terms of convergence speed over current state-of-the-art methods

Joint source-channel coding/decoding of 3D-ESCOT bitstreams

International audienceJoint source-channel decoding (JSCD) exploits residual redundancy in compressed bitstreams to improve the robustness to transmission errors of multimedia coding schemes. This paper proposes an architecture to introduce some additional side information in compressed streams to help JSCD. This architecture exploits a reference decoder already present or introduced at the encoder side. An application to the robust decoding of 3D-ESCOT encoded bitstreams generated within the Vidwav video coder is presented. The layered bitstream generated by this encoder allows SNR scalability, and moreover, when processed by a JSCD, provides increased robustness to transmission errors compared with a single layered bitstream

Reconstruction cohérente de l'entrée d'un banc de filtres suréchantillonnés à partir de sa sortie bruitée

International audienceIn this paper we introduce a reconstruction approach for the input signal of an oversampled filter bank (OFB) when the subbands generated at its output are quantized and transmitted over a noisy channel. We exploit the redundancy introduced by the OFB and the bounded quantization noise in order to construct a consistent estimator that corrects transmission errors. A maximum-likelihood estimation of the quantization indexes transmitted over the channel is evaluated, which only considers the vectors of quantization indexes corresponding to subband signals that could have been generated by the OFB and that are compliant with the quantization errors. Neither hypothesis tests nor specific parameters need to be set or computed in advance as is the case in approaches presented in \cite{Redinbo00,LabeauChiang2004}. When considering an OFB with oversampling ratio $3/2$, a BPSK modulation of the quantized subbands and a transmission over an AWGN channel, compared to a classical decoder, the gain is about $8$ dB in terms of reconstructed signal SNR for a channel SNR of $7$ dB

Adaptive lifting schemes with a global L1 minimization technique for image coding

International audienceMany existing works related to lossy-to-lossless image compression are based on the lifting concept. In this paper, we present a sparse op- timization technique based on recent convex algorithms and applied to the prediction filters of a two-dimensional non separable lifting structure. The idea consists of designing these filters, at each resolution level, by minimizing the sum of the ℓ1-norm of the three detail subbands. Extending this optimization method in order to perform a global minimization over all resolution levels leads to a new opti- mization criterion taking into account linear dependencies between the generated coefficients. Simulations carried out on still images show the benefits which can be drawn from the proposed optimization techniques

Two-dimensional non separable adaptive lifting scheme for still and stereo image coding

International audienceMany existing works related to lossy-to-lossless image compression are based on the lifting concept. However, it has been observed that the separable lifting scheme structure presents some limitations because of the separable processing performed along the image lines and columns. In this paper, we propose to use a 2D non separable lifting scheme decomposition that enables progressive reconstruction and exact decoding of images. More precisely, we focus on the optimization of all the involved decomposition operators. In this respect, we design the prediction filters by minimizing the variance of the detail signals. Concerning the update filters, we propose a new optimization criterion which aims at reducing the inherent aliasing artefacts. Simulations carried out on still and stereo images show the benefits which can be drawn from the proposed optimization of the lifting operators

Consistent Reconstruction of the Input of an Oversampled Filter Bank From Noisy Subbands

This paper introduces a reconstruction approach for the input signal of an oversampled filter bank (OFB) when the sub-bands generated at its output are quantized and transmitted over a noisy channel. This approach exploits the redundancy introduced by the OFB and the fact that the quantization noise is bounded. A maximum-likelihood estimate of the input signal is evaluated, which only considers the vectors of quantization indexes corresponding to subband signals that could have been generated by the OFB and that are compliant with the quantization errors. When considering an OFB with an oversampling ratio of 3/2 and a transmission of quantized subbands on an AWGN channel, compared to a classical decoder, the performance gains are up to 9 dB in terms of SNR for the reconstructed signal, and 3 dB in terms of channel SNR.Comment: European Signal Processing Conference (2011

MAP estimation of the input of an oversampled filter bank from noisy subbands by belief propagation

International audienceOversampled filter banks perform simultaneously subband decomposition and redundancy introduction. This redundancy has been shown to be useful to combat channel impairments, when the subbands are transmitted over a wireless channel, as well as quantization noise. This paper describes an implementation of the maximum \emph{a posteriori} and the minimum mean-square error (MMSE) estimators of the input signal from the noisy quantized subbands obtained at the output of some transmission channel. The relations between the input samples and the noisy subband samples are described using a factor graph. Belief propagation is then applied to get the posterior marginals of the input samples. The experimental results show that when the channel is clear, a linear MMSE estimate is satisfying. But, the proposed approaches perform significantly better than a reconstruction using the linear MMSE estimator when the channel is noisy: a gain in terms of channel SNR of more than $2$~dB is observed

ROBUST DECODING OF A 3D-ESCOT BITSTREAM TRANSMITTED OVER A NOISY CHANNEL

International audienceIn this paper, we propose a joint source-channel (JSC) decoding scheme for 3D ESCOT-based video coders, such as Vidwav. The embedded bitstream generated by such coders is very sensitive to transmission errors unavoidable on wireless channels. The proposed JSC decoder employs the residual redundancy left in the bitstream by the source coder combined with bit reliability information provided by the channel or channel decoder to correct transmission errors. When considering an AWGN channel, the performance gains are in average 4 dB in terms of PSNR of the reconstructed frames, and 0.7 dB in terms of channel SNR. When considering individual frames, the obtained gain is up to 15 dB in PSNR

Stochastic Fractal Models for Image Processing

International audienceOur study of fractal landscapes departs from the simplest but yet effective model of fractional Brownian motion and explores its two-dimensional (2-D) extensions. We focus on the ability to introduce anisotropy in this model, and we are also interested in considering its discrete-space counterparts. We then move towards other multifractional and multifractal models providing more degrees of freedom for fitting complex 2-D fields. We note that many of the models and processing are implemented in FracLab, a software MATLAB/Scilab toolbox for fractal processing of signals and images

State of the art in 2D content representation and compression

Livrable D1.3 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D3.1 du projet