Low bit-rate video compression with neural networks and temporal subsampling

Cramer, Christopher; Gelenbe, Erol; Bakircloglu, H.

doi:10.1109/5.537116

Cited by 76 publications

(39 citation statements)

References 38 publications

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“…This recently invented model [20,22,21] appears to capture accurately and robustly the function mapping the various parameters involved with the quality metric. RNN have been used in many different domains such as image and video compression [27,13,12], error-correcting codes [1], land mine detection [32], video quality assessment [52], where they proved themselves better than the ANN for this kind of application, and video quality enhancement [11,28]. A survey of RNN applications is given in [3].…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of real-time speech through a packet network: a random neural networks-based approach

Mohamed

Rubino

Varela

2004

Performance Evaluation

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This paper addresses the problem of quantitatively evaluating the quality of a speech stream transported over the Internet as perceived by the end user. We propose an approach being able to perform this task automatically and, if necessary, in real time. Our method is based on using G-networks (open networks of queues with positive and negative customers) as neural networks (in this case, they are called Random Neural Networks) to learn, in some sense, how humans react vis-a-vis a speech signal that has been distorted by encoding and transmission impairments. This can be used for control purposes, for pricing applications, etc.Our method allows us to study the impact of several source and network parameters on the quality, which appears to be new (previous work analyzes the effect of one or two selected parameters only). In this paper we use our technique to study the impact on performance of several basic source and network parameters on a non-interactive speech flow, namely loss rate, loss distribution, codec, forward error correction, and packetization interval, all at the same time. This is important because speech/audio quality is affected by several parameters whose combined effect is neither well identified nor understood.

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Section: Introductionmentioning

confidence: 99%

Performance evaluation of real-time speech through a packet network: a random neural networks-based approach

Mohamed

Rubino

Varela

2004

Performance Evaluation

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“…Some of its other applications can be found in [1,3,4,9,12,25,26,89,[101][102][103]157,174] and several papers reviewing this subject can be found in the papers of the special issue in [49].…”

Section: The Random Neural Networkmentioning

confidence: 99%

Erol Gelenbe: A Career in Multi-Disciplinary Probability Models

Caglayan¹

2016

Prob. Eng. Inf. Sci.

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We focus on Erol Gelenbe's scientific and technical contributions to probability models in the computer and information sciences, but limit our survey to the last fifteen years. We start with a brief overview of his work as a single author, as well as his work in collaboration with over 200 co-authors. We discuss some of his recent and innovative work regarding a new probability model that represents Intermittent Energy Sources for Computing and Communications, introducing Energy Packet Networks which are a probabilistic representation of the flow, storage and consumption of electrical energy at the microscopic level (in electronic chips), and at the macroscopic level (e.g. in buildings or data centers) and for its routing and dynamic usage by consuming units (such as computer elements, chips or machines). We next discuss his work on designing computer and communication systems that parsimoniously use energy in order to achieve a satisfactory level of quality of service (QoS). Trade-offs between system QoS and energy consumption are also considered. Then we turn to Prof. Gelenbe's pioneering work on Autonomic Communications and the design and implementation of CPN, the Cognitive Packet Network, and we also briefly review his spiking random neural network that was used in CPN. This is followed by a brief review of work that he conducted since 1999 on human evacuation from dangerous or catastrophic environments, and the design of technology driven Emergency Management Systems. His research since the late 2000s on Gene Regulatory Networks is then covered together with its application to the detecting possible disease from microarray data. Finally, we briefly discuss some novel analytical models that he developed in this period with publications appearing in journals of physics and applied mathematics.

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“…Other applications of the random neural network that do not require learning include function optimization (Gelenbe, Koubi, and Pekergin [99]) and texture generation (Atalay and Gelenbe [9], Atalay, Gelenbe, and Yalabik [10]). Applications of the RNN were published for video compression (Cramer, Gelenbe, and Bakircioglu [20,21]), complex recognition tasks (Abdelbaki, Gelenbe, and El-Khamy [1], Abdelbaki, Gelenbe, and Kocak [2], Abdelbaki et al [3], Aguilar and Gelenbe [8], Gelenbe, Ghanwani, and Srinivasan [85], Hocaoglu et al [155]), and to the sensory search of patterns and objects (Gelenbe and Cao [74], Gelenbe and Koçak [97], Gelenbe, Koçak, and Wang [98]). A polynomial time-complexity learning algorithm for RNNs having soma-to-soma interactions was first presented in (Gelenbe and Timotheou [142]) and is further developed in (Wang and Gelenbe [184]).…”

Section: Extensions and Applications Of The Random Neural Network (Rnn)mentioning

confidence: 99%

G-Networks and Their Applications to Machine Learning, Energy Packet Networks and Routing: Introduction to the Special Issue

Caglayan¹

2017

Prob. Eng. Inf. Sci.

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This paper introduces a special issue of this journal (Probability in the Engineering and Informational Sciences) that is devoted to G(elenbe)-Networks and their Applications. The special issue is based on revised versions of some of the papers that were presented at a workshop held in early January 2017 at the Séminaire Saint-Paul in Nice (France). It includes contributions in several research directions that followed from the introduction of the G-Network in the late 1980s. The papers present original theoretical developments, as well as applications of G-Networks to Machine Learning, to the performance optimization of energy systems via the novel Energy Packet Networks formalism for systems that operate with renewable and intermittent energy sources, and to packet network routing and Cloud management over the Internet. We introduce these contributions from the perspective of an overview of recent work based on G-Networks.

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Low bit-rate video compression with neural networks and temporal subsampling

Cited by 76 publications

References 38 publications

Performance evaluation of real-time speech through a packet network: a random neural networks-based approach

Performance evaluation of real-time speech through a packet network: a random neural networks-based approach

Erol Gelenbe: A Career in Multi-Disciplinary Probability Models

G-Networks and Their Applications to Machine Learning, Energy Packet Networks and Routing: Introduction to the Special Issue

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