DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression

We propose a new architecture for distributed image compression from a group of distributed data sources. The work is motivated by practical needs of data-driven codec design, low power consumption, robustness, and data privacy. The proposed architecture, which we refer to as Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC), is able to train distributed encoders and one joint decoder on correlated data sources. Its compression capability is much better than the method of training codecs separately. Meanwhile, the performance of our distributed system with 10 distributed sources is only within 2 dB peak signal-to-noise ratio (PSNR) of the performance of a single codec trained with all data sources. We experiment distributed sources with different correlations and show how our data-driven methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding (DSC). To the best of our knowledge, this is the first data-driven DSC framework for general distributed code design with deep learning

Distributed coding of endoscopic video

Triggered by the challenging prerequisites of wireless capsule endoscopic video technology, this paper presents a novel distributed video coding (DVC) scheme, which employs an original hash-based side-information creation method at the decoder. In contrast to existing DVC schemes, the proposed codec generates high quality side-information at the decoder, even under the strenuous motion conditions encountered in endoscopic video. Performance evaluation using broad endoscopic video material shows that the proposed approach brings notable and consistent compression gains over various state-of-the-art video codecs at the additional benefit of vastly reduced encoding complexity

ToyArchitecture: Unsupervised Learning of Interpretable Models of the World

Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are usually uncomputable, incompatible with theories of biological intelligence, or lack practical implementations. The goal of this work is to combine the main advantages of the two: to follow a big picture view, while providing a particular theory and its implementation. In contrast with purely theoretical approaches, the resulting architecture should be usable in realistic settings, but also form the core of a framework containing all the basic mechanisms, into which it should be easier to integrate additional required functionality. In this paper, we present a novel, purposely simple, and interpretable hierarchical architecture which combines multiple different mechanisms into one system: unsupervised learning of a model of the world, learning the influence of one's own actions on the world, model-based reinforcement learning, hierarchical planning and plan execution, and symbolic/sub-symbolic integration in general. The learned model is stored in the form of hierarchical representations with the following properties: 1) they are increasingly more abstract, but can retain details when needed, and 2) they are easy to manipulate in their local and symbolic-like form, thus also allowing one to observe the learning process at each level of abstraction. On all levels of the system, the representation of the data can be interpreted in both a symbolic and a sub-symbolic manner. This enables the architecture to learn efficiently using sub-symbolic methods and to employ symbolic inference.Comment: Revision: changed the pdftitl

Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Prefix Codes for Power Laws with Countable Support

In prefix coding over an infinite alphabet, methods that consider specific distributions generally consider those that decline more quickly than a power law (e.g., Golomb coding). Particular power-law distributions, however, model many random variables encountered in practice. For such random variables, compression performance is judged via estimates of expected bits per input symbol. This correspondence introduces a family of prefix codes with an eye towards near-optimal coding of known distributions. Compression performance is precisely estimated for well-known probability distributions using these codes and using previously known prefix codes. One application of these near-optimal codes is an improved representation of rational numbers.Comment: 5 pages, 2 tables, submitted to Transactions on Information Theor

THE VISNET II DVC CODEC: ARCHITECTURE, TOOLS AND PERFORMANCE

ABSTRACT This paper introduces the VISNET II DVC codec. This codec achieves very high RD performance thanks to the efficient combination of many state-of-the-art coding tools into INTRODUCTION With the wide deployment of mobile and wireless networks, a growing number of emerging applications, such as lowpower sensor networks, video surveillance cameras and mobile communications, rely on an up-link model rather than the typical down-link communication model. Typically, these applications are characterized by many senders transmitting data to a central receiver. In this context, light encoding or a flexible distribution of the codec complexity, robustness to packet losses, high compression efficiency and low latency/delay are important requirements. To address the needs of these up-link applications, the usual predictive video coding paradigm has been revisited based on Information Theory theorems from the 70s. The Slepian-Wolf (SW) theorem [1] establishes lower bounds on the achievable rates for the lossless coding of two or more correlated sources. More specifically, considering two statistically dependent random signals X and Y, it is well-known that the lower bound for the rate is given by the joint entropy H(X,Y) when these two signals are jointly encoded (as in conventional predictive coding). Conversely, when these two signals are independently encoded but jointly decoded (distributed coding), the SW theorem states that the minimum rate is still H(X,Y) with a residual error probability which tends towards 0 for long sequences. Later, Wyner and Ziv (WZ) have extended the SW theorem and showed that the result holds for the lossy coding case under the assumptions that the sources are jointly Gaussian and a mean square error distortion measure is used [2]. Subsequently, it was shown that this result remains valid as long as the difference between X and Y is Gaussian. Video coding schemes based on these theorems are referred to as Distributed Video Coding (DVC) solutions. Since the new coding paradigm is based on a statistical framework and does not rely on joint encoding, DVC architectures may provide several functional benefits which are rather important for many emerging applications: i) flexible allocation of the global video codec complexity; ii) improved error resilience; iii) codec independent scalability; and iv) exploitation of multiview correlation. Based on these theoretical results, practical implementations of DVC have been proposed since 2002. The PRISM (Power-efficient, Robust, hIgh compression Syndrome-based Multimedia coding) [3] solution works at the block level and performs motion estimation at the decoder. Based on the amount of temporal correlation, estimated using a zeromotion block difference, each block can either be conventionally (intra) coded, skipped or coded using distributed coding principles. Another DVC architecture working at frame level has been proposed in In this paper, the DVC codec developed within the European Network of Excellence VISNET II project [5] is described. This codec is based on the early architecture in VISNET II CODEC ARCHITECTURE AND TOOLS This section provides a description of the VISNET II DVC codec architecture and tools illustrated i

Graph Signal Processing: Overview, Challenges and Applications

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing. We then summarize recent developments in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE

Sparse Signal Processing Concepts for Efficient 5G System Design

As it becomes increasingly apparent that 4G will not be able to meet the emerging demands of future mobile communication systems, the question what could make up a 5G system, what are the crucial challenges and what are the key drivers is part of intensive, ongoing discussions. Partly due to the advent of compressive sensing, methods that can optimally exploit sparsity in signals have received tremendous attention in recent years. In this paper we will describe a variety of scenarios in which signal sparsity arises naturally in 5G wireless systems. Signal sparsity and the associated rich collection of tools and algorithms will thus be a viable source for innovation in 5G wireless system design. We will discribe applications of this sparse signal processing paradigm in MIMO random access, cloud radio access networks, compressive channel-source network coding, and embedded security. We will also emphasize important open problem that may arise in 5G system design, for which sparsity will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces