Rate-compatible punctured convolutional codes (RCPC codes) and their applications

Hagenauer, J.

doi:10.1109/26.2763

Cited by 1,608 publications

(893 citation statements)

References 17 publications

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“…The quantized data follows standard procedure like zigzag, Run Length and Huffman coding. Source Coded data passes through Rate Compatible Puncture Convolutional Coder (RCPC) [10] which is suitable for UEP application. In JSCC [13] system total transmit rate will be fixed.…”

Section: Overview Of Systemmentioning

confidence: 99%

“…So priority based channel protection can be allocated and that is the motivation to develop UEP. Using UEP significant gain performance improvement can be obtained compare to Equal Error Protection (EEP) [8]- [10]. The required steps for unequal error protection are: proper data partition and proper channel protection assignment according to importance of bit stream.…”

Section: Introductionmentioning

confidence: 99%

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Block Classification and Transmission of Compressed Image with Bit Allocation and Unequal Error Protection

Patel¹,

Patnaik²,

Mansi³

2016

IJCTE

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Abstract-In image/video transmission system block or macro block classification has frequently been used to classify similar types of properties of block like spectral, statistical, perceptual etc. The bit allocation algorithm distribute total number of bits among a finite set of quantizer to maximize the quality. This paper proposed joint source channel coding (JSCC) algorithm for image transmission with: (i) optimum bit allocation for classified blocks (ii) Unequal Error Protection (UEP) at channel coding stage to minimize the total distortion. The bit allocation applied at two levels. First distribute allocate source rate between three different classes and second each class individual transforms coefficient bit assignment. The simulation results shows that using this algorithm approximately 3-4 dB improvement compare to conventional UEP and further 1.5-2 dB improvement using optimum bit allocation algorithm.

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Section: Overview Of Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Block Classification and Transmission of Compressed Image with Bit Allocation and Unequal Error Protection

Patel¹,

Patnaik²,

Mansi³

2016

IJCTE

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“…At the emitter, source data are encoded through an UEP scheme [4] that can be achieved using rate-compatible channel encoder [6]. In this paper, we use Rate Compatible Punctured Convolutional (RCPC) codes are used, but it could easily be extended to any other ratecompatible encoding process (such as turbo codes or LDPC codes [7]).…”

Section: Transmission Modelmentioning

confidence: 99%

“…For poor channel conditions, this method appeals to a sub-frames truncation procedure combined with the matched modulation and error protection scheme on the frame to transmit. In the one hand, the UEP method ( [6] and [3]) adapts the encoding process to the required QoS starting from the most important classes to the least ones. In the other hand, throwing a part of the frame away allows to fulfill the TU size constraint.…”

Section: Introductionmentioning

confidence: 99%

Resources Allocation Optimization for Scalable Multimedia Data Subject to Quality of Service Contraints

Houas

Baras

Fijalkow

2006

2006 IEEE 7th Workshop on Signal Processing Advances in Wireless Communications

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Scalable multimedia data transmission are subject to specific constraints such as the Quality of Service (QoS) of sensitivity classes and the transmission rate (yielding a maximum size of each frame to send). Many scalable source decoders are used to discarding data than processing an erroneous stream. This featuring class structure is helpful to define a strategy that determines the maximum number of classes to send and delivers the suitable protection and transmission scheme (coding rates and modulation) to apply in accordance with the transmission constraints. It leads to the possible truncation of frame parts transmitted with an Unequal Error Protection (UEP) scheme for severe channel conditions. In a MPEG-4 speech frames context, we compare our approach to other methods using equal error and existing UEP schemes. It results in a significant improvement of the Peak SNR (PSNR) quality in poor channel transmission conditions.

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“…within the parameters of the error control coding scheme. In addition, unequal error protection can be provided by varying the number of bits used according to the priority of the data being protected [6,7].…”

Section: Previous Workmentioning

confidence: 99%

Error Concealment in Encoded Video Streams

Salama

Shroff

Delp

1998

Signal Recovery Techniques for Image and Video Compression and Transmission

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When transmitting compressed video over a data network, one has to deal with how channel errors affect the decoding process. This is particularly problematic with data loss or erasures. In this paper we describe techniques to address this problem in the context of networks where channel errors or congestion can result in the loss of entire macroblocks when MPEG video is transmitted. We describe spatial and temporal techniques for the recovery of lost macroblocks. In particular, we develop estimation techniques for the reconstruction of missing macroblocks using a Markov Random Field model. We show that the widely used heuristic motion compensated error concealment technique based on averaging motion vectors is a special case of our estimation technique. We further describe a technique that can be implemented in real-time.

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Rate-compatible punctured convolutional codes (RCPC codes) and their applications

Cited by 1,608 publications

References 17 publications

Block Classification and Transmission of Compressed Image with Bit Allocation and Unequal Error Protection

Block Classification and Transmission of Compressed Image with Bit Allocation and Unequal Error Protection

Resources Allocation Optimization for Scalable Multimedia Data Subject to Quality of Service Contraints

Error Concealment in Encoded Video Streams

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