Image and video compression

Cramer, Christopher; Gelenbe, Erol; Gelenbe, P.

doi:10.1109/45.652854

Cited by 32 publications

(11 citation statements)

References 6 publications

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“…For example, they are used in video compression with compression ratios that range from 500:1 to 1000:1 for moving gray-scale images and full-color video sequences, respectively [29]. They are also used as decoders for error-correcting codes in noisy communication channels [30].…”

Section: Related Workmentioning

confidence: 99%

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A study of real-time packet video quality using random neural networks

Mohamed

Rubino

2002

IEEE Trans. Circuits Syst. Video Technol.

212

130

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An important and unsolved problem today is that of automatic quantification of the quality of video flows transmitted over packet networks. In particular, the ability to perform this task in real time (typically for streams sent themselves in real time) is especially interesting. The problem is still unsolved because there are many parameters affecting video quality, and their combined effect is not well identified and understood. Among these parameters, we have the source bit rate, the encoded frame type, the frame rate at the source, the packet loss rate in the network, etc. Only subjective evaluations give good results but, by definition, they are not automatic. We have previously explored the possibility of using artificial neural networks (NNs) to automatically quantify the quality of video flows and we showed that they can give results well correlated with human perception. In this paper, our goal is twofold. First, we report on a significant enhancement of our method by means of a new neural approach, the random NN model, and its learning algorithm, both of which offer better performances for our application. Second, we follow our approach to study and analyze the behavior of video quality for wide range variations of a set of selected parameters. This may help in developing control mechanisms in order to deliver the best possible video quality given the current network situation, and in better understanding of QoS aspects in multimedia engineering.Index Terms-Packet video, random neural networks, real-time video transmission, video quality assessment, video signal characterization.

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Section: Related Workmentioning

confidence: 99%

“…The method we propose uses a new family of NNs, the so-called RNNs, recently invented by Erol Gelenbe [16]- [18] This choice was suggested by the success of this approach in many different areas [29]- [32], .…”

Section: B Random Neural Networkmentioning

confidence: 99%

A study of real-time packet video quality using random neural networks

Mohamed

Rubino

2002

IEEE Trans. Circuits Syst. Video Technol.

212

130

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“…It was originally introduced in [9] and extended in [10][11][12][13][14][15]. Although the RNN model was initially inspired by biophysical neural networks, it has been successfully applied in many areas such as associative memory [16][17][18], image processing [19][20][21], texture generation [22,23], video QoS and compression [19,[24][25][26][27][28][29], as well as task assignment [30] and resource allocation [31], and has inspired the use of negative customers in queuing networks, which has led to G-networks [32][33][34][35][36].…”

Section: Rnn-based Algorithmsmentioning

confidence: 99%

The Cognitive Packet Network: A Survey

Sakellari

2009

The Computer Journal

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Current and future multimedia networks require connections under specific quality of service (QoS) constraints which can no longer be provided by the best-effort Internet. Therefore, 'smarter'networks have been proposed in order to cover this need. The cognitive packet network (CPN) is a routing protocol that provides QoS-driven routing and performs self-improvement in a distributed manner, by learning from the experience of special packets, which gather on-line QoS measurements and discover new routes. The CPN was first introduced in 1999 and has been used in several applications since then. Here we provide a comprehensive survey of its variations, applications and experimental performance evaluations.

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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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Image and video compression

Cited by 32 publications

References 6 publications

A study of real-time packet video quality using random neural networks

A study of real-time packet video quality using random neural networks

The Cognitive Packet Network: A Survey

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

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