A Quality-of-Content-Based Joint Source and Channel Coding for Human Detections in a Mobile Surveillance Cloud

2017 ◽  
Vol 27 (1) ◽  
pp. 19-31 ◽  
Author(s):  
Xiang Chen ◽  
Jenq-Neng Hwang ◽  
De Meng ◽  
Kuan-Hui Lee ◽  
Ricardo L. de Queiroz ◽  
...  
Keyword(s):  
Channel Coding ◽  
Mobile Surveillance

Joint Source-Channel Coding in Dictionary Methods of Lossless Data Compression

2010 ◽  
Vol 56 (4) ◽  
pp. 351-355
Author(s):  
Marcin Rodziewicz
Keyword(s):  
Data Compression ◽  
Channel Coding ◽  
Data Stream ◽  
Noisy Channels ◽  
Error Resiliency ◽  
Joint Source Channel Coding ◽  
Lossless Data Compression ◽  
Compressed Data ◽  
Source Channel Coding

Joint Source-Channel Coding in Dictionary Methods of Lossless Data Compression Limitations on memory and resources of communications systems require powerful data compression methods. Decompression of compressed data stream is very sensitive to errors which arise during transmission over noisy channels, therefore error correction coding is also required. One of the solutions to this problem is the application of joint source and channel coding. This paper contains a description of methods of joint source-channel coding based on the popular data compression algorithms LZ'77 and LZSS. These methods are capable of introducing some error resiliency into compressed stream of data without degradation of the compression ratio. We analyze joint source and channel coding algorithms based on these compression methods and present their novel extensions. We also present some simulation results showing usefulness and achievable quality of the analyzed algorithms.

scholarly journals Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

2020 ◽  
Author(s):  
Md Jalil Piran
Keyword(s):  
Cognitive Radio ◽  
Channel Coding ◽  
Multimedia Communication ◽  
Radio Spectrum ◽  
Multimedia Communications ◽  
Future Research ◽  
Wireless Multimedia ◽  
Comprehensive Survey ◽  
Feasible Solutions

The stringent requirements of wireless multimedia<br>transmission lead to very high radio spectrum solicitation. Although the radio spectrum is considered as a scarce resource, the<br>issue with spectrum availability is not scarcity, but the inefficient<br>utilization. Unique characteristics of cognitive radio (CR) such<br>as flexibility, adaptability, and interoperability, particularly have<br>contributed to it being the optimum technological candidate to<br>alleviate the issue of spectrum scarcity for multimedia communications. However, multimedia communications over CR<br>networks (MCRNs) as a bandwidth-hungry, delay-sensitive, and<br>loss-tolerant service, exposes several severe challenges specially<br>to guarantee quality of service (QoS) and quality of experience<br>(QoE). As a result, to date, different schemes based on source and<br>channel coding, multicast, and distributed streaming, have been<br>examined to improve the QoS/QoE in MCRNs. In this paper,<br>we survey QoS/QoE provisioning schemes in MCRNs. We first<br>discuss the basic concepts of multimedia communication, CRNs,<br>QoS and QoE. Then, we present the advantages of utilizing CR<br>for multimedia services and outline the stringent QoS and QoE<br>requirements in MCRNs. Next, we classify the critical challenges<br>for QoS/QoE provisioning in MCRNs including spectrum sensing,<br>resource allocation management, network fluctuations management, latency management, and energy consumption management. Then, we survey the corresponding feasible solutions for<br>each challenge highlighting performance issues, strengths, and<br>weaknesses. Furthermore, we discuss several important open<br>research problems and provide some avenues for future research. <br>

scholarly journals Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

2020 ◽  
Author(s):  
Md Jalil Piran
Keyword(s):  
Cognitive Radio ◽  
Channel Coding ◽  
Multimedia Communication ◽  
Radio Spectrum ◽  
Multimedia Communications ◽  
Future Research ◽  
Wireless Multimedia ◽  
Comprehensive Survey ◽  
Feasible Solutions

The stringent requirements of wireless multimedia<br>transmission lead to very high radio spectrum solicitation. Although the radio spectrum is considered as a scarce resource, the<br>issue with spectrum availability is not scarcity, but the inefficient<br>utilization. Unique characteristics of cognitive radio (CR) such<br>as flexibility, adaptability, and interoperability, particularly have<br>contributed to it being the optimum technological candidate to<br>alleviate the issue of spectrum scarcity for multimedia communications. However, multimedia communications over CR<br>networks (MCRNs) as a bandwidth-hungry, delay-sensitive, and<br>loss-tolerant service, exposes several severe challenges specially<br>to guarantee quality of service (QoS) and quality of experience<br>(QoE). As a result, to date, different schemes based on source and<br>channel coding, multicast, and distributed streaming, have been<br>examined to improve the QoS/QoE in MCRNs. In this paper,<br>we survey QoS/QoE provisioning schemes in MCRNs. We first<br>discuss the basic concepts of multimedia communication, CRNs,<br>QoS and QoE. Then, we present the advantages of utilizing CR<br>for multimedia services and outline the stringent QoS and QoE<br>requirements in MCRNs. Next, we classify the critical challenges<br>for QoS/QoE provisioning in MCRNs including spectrum sensing,<br>resource allocation management, network fluctuations management, latency management, and energy consumption management. Then, we survey the corresponding feasible solutions for<br>each challenge highlighting performance issues, strengths, and<br>weaknesses. Furthermore, we discuss several important open<br>research problems and provide some avenues for future research. <br>

scholarly journals Iris Code Capacity Analysis

2021 ◽  
Author(s):  
Jinyu Zuo ◽  
Natalia Schmid
Keyword(s):  
Channel Coding ◽  
Hamming Distance ◽  
Rate Distortion ◽  
Recognition System ◽  
Performance Limits ◽  
Minimum Hamming Distance ◽  
Code Capacity ◽  
The Relationship ◽  
Research World

Daugman’s design of IrisCode continues fascinating the research world with its practicality, efficiency, and outstanding performance. The limits of Daugman’s recognition system, however, remain unquantified. Multiple approaches to scale performance have been explored in the past. Despite them, the problem of finding the capacity of IrisCode remains open.<br>In an attempt to fill the gap in understanding the performance limits of Daugman’s algorithm, we turn to an analysis of the relationship between the size of the population that the IrisCode can effectively cover and the iris sample quality. Given Daugman’s IrisCode algorithm, the problem of finding its capacity is cast as a basic Rate-Distortion/Channel Coding problem. The Hamming, Plotkin, and Elias-Bassalygo upper bounds on the population of a binary code under the constraint of a minimum Hamming Distance between any two codewords is applied to relate the number of iris classes that the IrisCode algorithm can sustain and the quality of iris data expressed in terms of Hamming Distance.<br><br>

scholarly journals INFORMATION-THEORETIC DATA-HIDING: RECENT ACHIEVEMENTS AND OPEN PROBLEMS

2005 ◽  
Vol 05 (01) ◽  
pp. 5-35 ◽  
Author(s):  
SVIATOSLAV VOLOSHYNOVSKIY ◽  
FREDERIC DEGUILLAUME ◽  
OLEKSIY KOVAL ◽  
THIERRY PUN
Keyword(s):  
Data Hiding ◽  
Channel Coding ◽  
Source Coding ◽  
Side Information ◽  
Digital Data ◽  
Open Problems ◽  
Information Theoretic ◽  
Public Network ◽  
Digital Data Hiding

In this paper we introduce and develop a framework for visual data-hiding technologies that aim at resolving emerging problems of modern multimedia networking. First, we introduce the main open issues of public network security, quality of services control and secure communications. Secondly, we formulate digital data-hiding into visual content as communications with side information and advocate an appropriate information-theoretic framework for the analysis of different data-hiding methods in various applications. In particular, Gel'fand-Pinsker channel coding with side information at the encoder and Wyner-Ziv source coding with side information at the decoder are used for this purpose. Finally, we demonstrate the possible extensions of this theory for watermark-assisted multimedia processing and indicate its perspectives for distributed communications.

scholarly journals The Analysis of Mobile WiMAX and LTE using MIMO technique

2013 ◽  
Vol 5 (1) ◽  
pp. 35-40
Author(s):  
Roopali Garg ◽  
Shafi Singla
Keyword(s):  
Channel Coding ◽  
Multiple Input Multiple Output ◽  
Spatial Multiplexing ◽  
Mobile Wimax ◽  
Ieee 802.16E ◽  
File Transfer ◽  
Network Resources ◽  
Multiple Input ◽  
Input Multiple Output

A quality of service is a fundamental component of the 4G broadband network for satisfactory service delivery by evolving internet application to end user, and managing the network resources. The 4G technology has emerged one of the most fruitful technologies as it supports large number of applications including VOIP, video conferencing, file transfer, video streaming and web browsing. IEEE 802.16E, IEEE 802.16M and LTE along with various application and QoS requirement also support Multiple-Input-multiple-output (MIMO) techniques including Spatial Multiplexing (SM), Space Time Block Coding(STBC) and Eigen Beam-forming (BM).In the paper we will study Physical, MAC layer of WiMAX and LTE. We will also study MIMO techniques. In this paper  the detailed study of the throughput for a MIMO mobile WiMAX system and LTE  under two different PHY PER QoS threshold. Various modulation and channel coding techniques have been applied.

scholarly journals Iris Code Capacity Analysis

2021 ◽  
Author(s):  
Jinyu Zuo ◽  
Natalia Schmid
Keyword(s):  
Channel Coding ◽  
Hamming Distance ◽  
Rate Distortion ◽  
Recognition System ◽  
Performance Limits ◽  
Minimum Hamming Distance ◽  
Code Capacity ◽  
The Relationship ◽  
Research World

Daugman’s design of IrisCode continues fascinating the research world with its practicality, efficiency, and outstanding performance. The limits of Daugman’s recognition system, however, remain unquantified. Multiple approaches to scale performance have been explored in the past. Despite them, the problem of finding the capacity of IrisCode remains open.<br>In an attempt to fill the gap in understanding the performance limits of Daugman’s algorithm, we turn to an analysis of the relationship between the size of the population that the IrisCode can effectively cover and the iris sample quality. Given Daugman’s IrisCode algorithm, the problem of finding its capacity is cast as a basic Rate-Distortion/Channel Coding problem. The Hamming, Plotkin, and Elias-Bassalygo upper bounds on the population of a binary code under the constraint of a minimum Hamming Distance between any two codewords is applied to relate the number of iris classes that the IrisCode algorithm can sustain and the quality of iris data expressed in terms of Hamming Distance.<br><br>

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