Combined Source-Channel Coding of Images

Modestino, J.W.; Daut, D.G.

doi:10.1109/tcom.1979.1094335

Cited by 136 publications

(38 citation statements)

References 19 publications

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“…This section provides an overview of channel encoding for still image communication over the channel. Recent developments in the wireless communication and mobile computing have sparked interest in the area of multimedia communication over wireless channels (Modestino & Daut, 1979), (Modestino et al, 1981), (Sabir et al, 2006). The state-of-the-art encoding and decoding methods rely on joint source channel coding for optimal communication performance.…”

Section: Channel Coding Of Imagesmentioning

confidence: 99%

Source Coding and Channel Coding for Mobile Multimedia Communication

Dilpazir¹,

Mahmood²,

Shah³

et al. 2012

Mobile Multimedia - User and Technology Perspectives

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Section: Channel Coding Of Imagesmentioning

confidence: 99%

Source Coding and Channel Coding for Mobile Multimedia Communication

Dilpazir¹,

Mahmood²,

Shah³

et al. 2012

Mobile Multimedia - User and Technology Perspectives

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“…A channel code is then designed for this source code to minimize end-to-end distortion over the given channel, which is typically a binary symmetric channel (BSC), an additive white Gaussian noise (AWGN) channel with a given modulation, or a time-varying channel. Modestino and Daut provided an early treatment of source-optimized channel coding using differential pulse code modulation (DPCM) for image coding followed by convolutional channel coding [7]. They concluded that near optimal performance can be achieved using equal-error-protection channel codes.…”

Section: Joint Source-channel Codingmentioning

confidence: 99%

Optimal multiresolution quantization for broadcast channels with random index assignment

Teng

Yang

2010

2010 IEEE International Symposium on Information Theory

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Shannon's classical separation result holds only in the limit of infinite source code dimension and infinite channel code block length. In addition, Shannon theory does not address the design of good source codes when the probability of channel error is nonzero, which is inevitable for finite-length channel codes. Thus, for practical systems, a joint source and channel code design could improve performance for finite dimension source code and finite block length channel code, as well as complexity and delay. Consider a multicast system over a broadcast channel, where different end users typically have different capacities. To support such user or capacity diversity, it is desirable to encode the source to be broadcasted into a scalable bit stream along which multiple resolutions of the source can be reconstructed progressively from left to right. Such source coding technique is called multiresolution source coding. In wireless communications, joint source channel coding (JSCC) has attracted wide attention due to its adaptivity to timevarying channels. However, there are few works on joint source channel coding for network multicast, especially for the optimal source coding over broadcast channels.In this work, we aim at designing and analyzing the optimal multiresolution vector quantization (MRVQ) in conjunction with the subsequent broadcast channel over which the coded scalable bit stream would be transmitted. By adopting random index assignment (RIA) to link MRVQ for the source with superposition coding for the broadcast channel, we establish a closed-form formula of end-to-end distortion for a tandem system of MRVQ and a broadcast channel. From this formula we analyze the intrinsic structure of end-to-end distortion (EED) in a communication system and derive two necessary conditions for optimal multiresolution vector quantization over broadcast channels with random index assignment. According to the two necessary conditions, we propose a greedy iterative algorithm for jointly designed MRVQ with channel conditions, which depends on the channel only through several types of average channel error probabilities rather than the complete knowledge of the channel. Experiments show that MRVQ designed by the proposed algorithm significantly outperforms conventional MRVQ designed without channel information.By building an closed-form formula for the weighted EED with RIA, it also makes the computational complexity incurred during the performance analysis feasible. In comparison with MRVQ design for a fixed index assignment, the computation complexity for quantization design is significantly reduced by using random index assignment. In addition, simulations indicate that our proposed algorithm shows better robustness against channel mismatch than MRVQ design with a fixed index assignment, simply due to the nature of using only the average channel information. Therefore, we conclude that our proposed algorithm is more appropriate in both wireless communications and applications where the complete knowledge of the...

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“…Systems involving transmission of video sources over rate constrained channels actually violate the conditions upon which the optimality of the so called "separation theorem" derived from Shannon's theory [2] relies, and performance improvements may be achieved by moving from separate design and operation of source and channel codes to joint source-channel coding (JSCC) [1,3]. JSCC/D techniques that include a co-ordination between source and channel encoders have been investigated, for example for transmission of audio data [4], images [5], and video [6,7]. Some of these works adapt the transmission to the source characteristics (unequal error protection, UEP), either at the channel coding level or through source adaptive modulation (see e.g., [4]).…”

Section: Introductionmentioning

confidence: 99%

“…Some of these works adapt the transmission to the source characteristics (unequal error protection, UEP), either at the channel coding level or through source adaptive modulation (see e.g., [4]). JSCC/D techniques may also require the use of rate/distortion curves or models of the source in order to perform the optimal compromise between source compression and channel protection [5].…”

Section: Introductionmentioning

confidence: 99%

Content adaptive network aware joint optimization of wireless video transmission

et al. 2007

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A content adaptive, network aware approach for joint optimization of wireless video transmission is presented in this article. The joint system optimization approach has been developed in the framework of the IST PHOENIX project (www.ist-phoenix.org).After a description of the considered crosslayer approach and of the information to be exchanged among the system component blocks, the concept of "JSCC/D controllers" is introduced. Results obtained through the demonstrator realized as proof-of-concept for the described paradigm are then shown, confirming the validity of the described joint optimization approach.

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Combined Source-Channel Coding of Images

Cited by 136 publications

References 19 publications

Source Coding and Channel Coding for Mobile Multimedia Communication

Source Coding and Channel Coding for Mobile Multimedia Communication

Optimal multiresolution quantization for broadcast channels with random index assignment

Content adaptive network aware joint optimization of wireless video transmission

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