Synchronization recovery and state model reduction for soft decoding of variable length codes

Variable length codes exhibit de-synchronization problems when transmitted over noisy channels. Trellis decoding techniques based on Maximum A Posteriori (MAP) estimators are often used to minimize the error rate on the estimated sequence. If the number of symbols and/or bits transmitted are known by the decoder, termination constraints can be incorporated in the decoding process. All the paths in the trellis which do not lead to a valid sequence length are suppressed. This paper presents an analytic method to assess the expected error resilience of a VLC when trellis decoding with a sequence length constraint is used. The approach is based on the computation, for a given code, of the amount of information brought by the constraint. It is then shown that this quantity as well as the probability that the VLC decoder does not re-synchronize in a strict sense, are not significantly altered by appropriate trellis states aggregation. This proves that the performance obtained by running a length-constrained Viterbi decoder on aggregated state models approaches the one obtained with the bit/symbol trellis, with a significantly reduced complexity. It is then shown that the complexity can be further decreased by projecting the state model on two state models of reduced size

Fast Deep Matting for Portrait Animation on Mobile Phone

Image matting plays an important role in image and video editing. However, the formulation of image matting is inherently ill-posed. Traditional methods usually employ interaction to deal with the image matting problem with trimaps and strokes, and cannot run on the mobile phone in real-time. In this paper, we propose a real-time automatic deep matting approach for mobile devices. By leveraging the densely connected blocks and the dilated convolution, a light full convolutional network is designed to predict a coarse binary mask for portrait images. And a feathering block, which is edge-preserving and matting adaptive, is further developed to learn the guided filter and transform the binary mask into alpha matte. Finally, an automatic portrait animation system based on fast deep matting is built on mobile devices, which does not need any interaction and can realize real-time matting with 15 fps. The experiments show that the proposed approach achieves comparable results with the state-of-the-art matting solvers.Comment: ACM Multimedia Conference (MM) 2017 camera-read

Error recovery properties of quasi-arithmetic codes and soft decoding with length constraint

International audienceNo abstrac

Le territoire du Grand Site de France Solutré Pouilly Vergisson : quelle gouvernance de ce bien commun paysager: les apports de l’enquête qualitative auprès des acteurs locaux

International audienc

Trellis state aggregation for soft decoding of variable length codes

Abstract — This paper describes a new set of state models for soft decoding of Variable Length Codes. A single parameter T allows to trade complexity against estimation accuracy. The extrema choices for this parameter lead respectively to construct the well-known bit-level and bit/symbol trellises. For a proper choice of the parameter T, the results obtained by running a BCJR or Viterbi estimation algorithm on the proposed state models are close to those obtained with the optimum state model. The complexity is however significantly reduced. It can be further decreased by projecting the state model on two state models of reduced size, and by combining their decoding results. This combination is shown to be optimal for the Viterbi algorithm. I