Video Compression for Camera Networks: A Distributed Approach

The problem of finding efficient communications techniques to distribute multi-view video content across different devices and users in a network is receiving a great attention in the last years. Much interest in particular has been devoted recently to the so called field of Distributed Video Coding (DVC). After briefly reporting traditional approaches to multiview coding, this chapter will introduce the field of DVC for multi-camera systems. The theoretical background of Distributed Source Coding (DSC) is first concisely presented and the problem of the application of DSC principles to the case of video sources is then analyzed. The topic is presented discussing approaches to the problem of DVC in both single-view and in multi-view applications

Minimal Information Exchange for Image Registration

In this paper we consider the problem of estimating the relative shift, scale and rotation between two images X and Y that are available to two users, respectively A and B, connected through a channel. User A is asked to send B some specifically selected minimal description of image X that will allow B to recover the relative shift, rotation and scale between X and Y. The approach is based on a distributed encoding technique applied to the Discrete Fourier Transform phase and to the Fourier-Mellin transform of the images

Adaptive Key frame Rate Allocation for Distributed Video Coding

In the context of Distributed Video Coding (DVC), rate allocation among sources represents an important problem to solve. While in the information theoretical setting of Distributed Source Coding (DSC) the statistical correlation between sources is usually assumed to be known, in practical DVC systems there is no a priori knowledge of the underlying statistics of visual data. This lack of information makes it difficult to deal with the problem of rate allocation in practical DVC codecs. In this paper we focus on the problem of how to distribute the rate between differently encoded parts of the video sequence in a DVC system. Namely, we propose an adaptive rate allocation scheme for the encoding of the key frames depending on an estimation of the local motion activity of the sequence

Coherent video reconstruction with motion estimation at the decoder

In traditional predictive video coding the block matching is performed at the encoder. The obtained motion field is then transmitted to the decoder, together with the prediction residue. Nevertheless, if the motion field is not provided it can be reconstructed, as long as the decoder manages to exploit some correlated information. This paper presents an algorithm for the motion estimation at the decoder side, given the prediction residue only. The main novelty of this algorithm relies on the contextual reconstruction of a frame region composed of several blocks. Simulation results show that taking into account a whole row can improve significantly the results obtained with an algorithm that reconstructs each block separately

Mismatched Decoding Reliability Function at Zero Rate

We derive an upper bound on the reliability function of mismatched decoding for zero-rate codes. The bound is based on a result by Koml ́os that shows the existence of a subcode with certain symmetry properties. The bound is shown to coincide with the expurgated exponent at rate zero for a broad family of channel-decoding metric pairs.ERC grant ITU

Distributed Coding of Shifts Using the DFT Phase

In this paper we consider the problem of image encoding with side information at the decoder, where the side information is an integer shifted version of the image at the encoder. The encoder is asked to send the shift of its own image with respect to the side information which is only available at the decoder. We propose a solution based on the encoding of the phase sign of the DFT coefficients, taken at exponentially spaced positions. We first introduce the method under ideal hypothesis, i.e. noiseless conditions without border effects, giving a theoretical foundation to the technique. Then, we consider the more realistic case of noisy images with border effects, showing the effectiveness of the proposed method

Efficient digital pre-filtering for least squares linear approximation

In this paper we propose a very simple FIR pre-filter for near optimal least-squares linear approximation of discrete time signals. At first, a greedy least square approximation of the desired signal is derived using an efficient digital pre-processing filter, then the usual linear interpolation is applied to obtain the final result. This leads to a non interpolating reconstruction of the signal, with good reconstruction quality and very limited computational cost. The basic formalism adopted to design the pre-filter has been derived from the framework introduced by Blu et Unser. To demonstrate the usability and the effectiveness of the approach, the proposed method has been applied to the problem of natural image resampling, which is typically applied when the image undergoes successive rotations. The performance obtained are very interesting, and the required computational effort is extremely low

Efficient Digital Pre-Filtering For Least-Squares Linear Approximation

In this paper we propose a very simple FIR pre-filter based method for near optimal least-squares linear approximation of discrete time signals. A digital pre-processing filter, which we demonstrate to be near-optimal, is applied to the signal before performing the usual linear interpolation. This leads to a non interpolating reconstruction of the signal, with good reconstruction quality and very limited computational cost. The basic formalism adopted to design the pre-filter has been derived from the framework introduced by Blu et Unser in [1]. To demonstrate the usability and the effectiveness of the approach, the proposed method has been applied to the problem of natural image resampling, which is typically applied when the image undergoes successive rotations. The performance obtained are very interesting, and the required computational effort is extremely low

Consistent Image Decoding from Multiple Lossy Versions

With the recent development of tools for data sharing in social networks and peer to peer networks, the same information is often stored in different nodes. Peer-to-peer protocols usually allow one user to collect portions of the same file from different nodes in the network, substantially improving the rate at which data are received by the end user. In some cases, however, the same multimedia document is available in different lossy versions on the network nodes. In such situations, one may be interested in collecting all available versions of the same document and jointly decoding them to obtain a better reconstruction of the original. In this paper we study some methods to jointly decode different versions of the same image. We compare different uses of the method of Projections Onto Convex Sets (POCS) with some Convex Optimization techniques in order to reconstruct an image for which JPEG and JPEG2000 lossy versions are available