43 research outputs found
Source-Channel Diversity for Parallel Channels
We consider transmitting a source across a pair of independent, non-ergodic
channels with random states (e.g., slow fading channels) so as to minimize the
average distortion. The general problem is unsolved. Hence, we focus on
comparing two commonly used source and channel encoding systems which
correspond to exploiting diversity either at the physical layer through
parallel channel coding or at the application layer through multiple
description source coding.
For on-off channel models, source coding diversity offers better performance.
For channels with a continuous range of reception quality, we show the reverse
is true. Specifically, we introduce a new figure of merit called the distortion
exponent which measures how fast the average distortion decays with SNR. For
continuous-state models such as additive white Gaussian noise channels with
multiplicative Rayleigh fading, optimal channel coding diversity at the
physical layer is more efficient than source coding diversity at the
application layer in that the former achieves a better distortion exponent.
Finally, we consider a third decoding architecture: multiple description
encoding with a joint source-channel decoding. We show that this architecture
achieves the same distortion exponent as systems with optimal channel coding
diversity for continuous-state channels, and maintains the the advantages of
multiple description systems for on-off channels. Thus, the multiple
description system with joint decoding achieves the best performance, from
among the three architectures considered, on both continuous-state and on-off
channels.Comment: 48 pages, 14 figure
Robust video coder solution for wireless streaming: applications in Gaussian channels
With the technological progress in wireless communications seen in the past decade, the miniaturization of personal computers was imminent. Due to the limited availability of resources in these small devices, it has been preferable to stream the media over widely deployed networks like the Internet. However, the conventional protocols used in physical and data-link layers are not adequate
for reliable video streaming over noisy wireless channels. There are several popular and well-studied mechanisms for addressing this problem, one of them being Multiple-Description-Coding. However, proposed solutions are too specialized, focusing the coding of either motion or spatial information; thus failing to address the whole problem, that is - the robust video coding.
In this thesis a novel MDC video coder is presented, which was developed during an internship at the I3S laboratory - France. The full coding scheme is capable of robust transmission of Motion-Vectors and wavelet-subband information over noisy wireless channels. The former is accomplished
by using a MAP-based MD-decoding algorithm available in literature, while the robust transmission of wavelet-subbands is achieved using a state-of-the-art registry-based JPEG-2000 MDC. In order to e ciently balance MV information between multiple descriptions, a novel R/D-optimizing MD bitallocation
scheme is presented. As it is also important to e ciently distribute bits between subband
and motion information, a global subband/motion-vector bit-allocation technique found in literature was adopted and improved. Indeed, this thesis would not be complete without the presentation of
produced streams as well as of a set of backing scienti c results
Fusion of Global and Local Motion Estimation Using Foreground Objects for Distributed Video Coding
International audienceThe side information in distributed video coding is estimated using the available decoded frames, and exploited for the decoding and reconstruction of other frames. The quality of the side information has a strong impact on the performance of distributed video coding. Here we propose a new approach that combines both global and local side information to improve coding performance. Since the background pixels in a frame are assigned to global estimation and the foreground objects to local estimation, one needs to estimate foreground objects in the side information using the backward and forward foreground objects, The background pixels are directly taken from the global side information. Specifically, elastic curves and local motion compensation are used to generate the foreground objects masks in the side information. Experimental results show that, as far as the rate-distortion performance is concerned, the proposed approach can achieve a PSNR improvement of up to 1.39 dB for a GOP size of 2, and up to 4.73 dB for larger GOP sizes, with respect to the reference DISCOVER codec. Index Terms A. ABOU-ELAILAH, F. DUFAUX, M. CAGNAZZO, and B. PESQUET-POPESCU are with the Signal and Image Processin
Convexity and Operational Interpretation of the Quantum Information Bottleneck Function
In classical information theory, the information bottleneck method (IBM) can
be regarded as a method of lossy data compression which focusses on preserving
meaningful (or relevant) information. As such it has recently gained a lot of
attention, primarily for its applications in machine learning and neural
networks. A quantum analogue of the IBM has recently been defined, and an
attempt at providing an operational interpretation of the so-called quantum IB
function as an optimal rate of an information-theoretic task, has recently been
made by Salek et al. However, the interpretation given in that paper has a
couple of drawbacks; firstly its proof is based on a conjecture that the
quantum IB function is convex, and secondly, the expression for the rate
function involves certain entropic quantities which occur explicitly in the
very definition of the underlying information-theoretic task, thus making the
latter somewhat contrived. We overcome both of these drawbacks by first proving
the convexity of the quantum IB function, and then giving an alternative
operational interpretation of it as the optimal rate of a bona fide
information-theoretic task, namely that of quantum source coding with quantum
side information at the decoder, and relate the quantum IB function to the rate
region of this task. We similarly show that the related privacy funnel function
is convex (both in the classical and quantum case). However, we comment that it
is unlikely that the quantum privacy funnel function can characterize the
optimal asymptotic rate of an information theoretic task, since even its
classical version lacks a certain additivity property which turns out to be
essential.Comment: 17 pages, 7 figures; v2: improved presentation and explanations, one
new figure; v3: Restructured manuscript. Theorem 2 has been found previously
in work by Hsieh and Watanabe; it is now correctly attribute
Analog Multiple Descriptions: A Zero-Delay Source-Channel Coding Approach
This paper extends the well-known source coding problem of multiple
descriptions, in its general and basic setting, to analog source-channel coding
scenarios. Encoding-decoding functions that optimally map between the (possibly
continuous valued) source and the channel spaces are numerically derived. The
main technical tool is a non-convex optimization method, namely, deterministic
annealing, which has recently been successfully used in other mapping
optimization problems. The obtained functions exhibit several interesting
structural properties, map multiple source intervals to the same interval in
the channel space, and consistently outperform the known competing mapping
techniques.Comment: Submitted to ICASSP 201
Sequential coding of Gauss-Markov sources with packet erasures and feedback
We consider the problem of sequential transmission of Gauss-Markov sources. We show that in the limit of large spatial block lengths, greedy compression with respect to the squared error distortion is optimal; that is, there is no tension between optimizing the distortion of the source in the current time instant and that of future times. We then extend this result to the case where at time t a random compression rate rt is allocated independently of the rate at other time instants. This, in turn, allows us to derive the optimal performance of sequential coding over packet-erasure channels with instantaneous feedback. For the case of packet erasures with delayed feedback, we connect the problem to that of compression with side information that is known at the encoder and may be known at the decoder — where the most recent packets serve as side information that may have been erased, and demonstrate that the loss due to a delay by one time unit is rather small