Using 2:1 Shannon Mapping for Joint Source-Channel Coding

Hekland, F.; Oien, G.E.; Ramstad, T.A.

doi:10.1109/dcc.2005.92

Cited by 69 publications

(71 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By selecting the direction of mapping, one can achieve N : 1 bandwidth compression or 1 : N bandwidth expansion. It is known that for i.i.d zero mean Gaussian sources 2:1 mappings using the Archimedes' spiral can achieve quasioptimal performance [10]. The Archimedes' Spiral (single arm for θ >0) can be described by the following parametric form:…”

Section: Analog Joint Source Channel Codingmentioning

confidence: 99%

Enhancing the Protection of Digital Images against Tampering Using Joint Source Channel Coding

2016

IJSR

View full text Add to dashboard Cite

Abstract:With the rapid development of multimedia products, digital images can be modified easily. This questions the integrity of images. To provide the integrity and authenticity to images, digital watermarking can be effectively used. In addressing the problem of image tampering these algorithms must be enough to accommodate two functionalities such as tampering detection and error concealment. Both these functions can be achieved by using check bits and reference bits as watermark data. Check bits carry information for the tampering detection, whereas reference bits carry information about the whole image which can be used for recovering the lost information. Check bits are MD5 hash output which can detect the tampering that also results in tampering localization. The generation of reference bits can be done by analog joint source channel coding of the image. Analog joint source channel coding directly maps analog image symbols to channel symbols without requiring source coding followed by channel coding. This reduces the computational complexities. This paper presents a novel and effective method for tampering detection and image self recovery.

show abstract

Section: Analog Joint Source Channel Codingmentioning

confidence: 99%

Enhancing the Protection of Digital Images against Tampering Using Joint Source Channel Coding

2016

IJSR

View full text Add to dashboard Cite

show abstract

“…7-9), described for a given t by (3) and (5), respectively; and the HDA-II bounds for both matched and mismatched cases (shown in Fig. 9), described for a given t by (11) and (12), respectively. We can observe the following:…”

Section: Gauss-markov Source)mentioning

confidence: 99%

“…An improved image coding system is proposed in [6]; it utilizes both bandwidth compression and bandwidth expansion mappings, where the bandwidth expansion mapping employs a scalar quantizer and transmits both the quantized value and the quantization error. Recently, a JSCC technique known as the 2:1 Shannon mapping was investigated in [11] and shown to provide very robust performance. It employs the Archimedean spiral to approximately map a point in a plane onto a point on a line.…”

Section: Introductionmentioning

confidence: 99%

“…Schemes that exploit the advantage of analog systems are studied by Ramstad and his co-authors in [16], [10], [6], [5], and [11]. These are based on the so-called direct source-channel mapping technique: the output of a source scalar/vector quantizer is mapped directly to a channel symbol using analog (or nearly analog) modulation, i.e., amplitude modulation or M -ary quadrature amplitude modulation (QAM) with M 1.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid digital-analog coding with bandwidth compression for gaussian source-channel pairs

Wang

Alajaji

Linder

2009

IEEE Trans. Commun.

View full text Add to dashboard Cite

Abstract-Three hybrid digital-analog (HDA) systems, denoted by HDA-I, HDA * and HDA-II, for the coding of a memoryless discrete-time Gaussian source over a discrete-time additive memoryless Gaussian channel under bandwidth compression are studied. The systems employ simple linear coding in their analog component and superimpose their analog and digital signals before channel transmission. Information-theoretic upper bounds on the asymptotically optimal mean squared error distortion of the systems are obtained under both matched and mismatched channel conditions. Allocation schemes for distributing the channel input power between the analog and the digital signals are also examined. It is shown that systems HDA * and HDA-II can asymptotically achieve the optimal Shannon-limit performance under matched channel conditions. Low-complexity and lowdelay versions of systems HDA-I and HDA-II are next designed and implemented without the use of error correcting codes. The parameters of these HDA systems, which employ vector quantization in conjunction with binary phase-shift keying modulation in their digital part, are optimized via an iterative algorithm similar to the design algorithm for channel-optimized vector quantizers. Both systems have low complexity and low delay, and guarantee graceful performance improvements for high CSNRs. For memoryless Gaussian sources the designed HDA-II system is shown to be superior to the HDA-I designed system. When applied to a Gauss-Markov source under Karhunen-Loeve processing, the HDA-I system is shown to provide considerably better performance.

show abstract

“…In a similar flavor, [26] realize the SC and CC with nonlinear transforms with dimensionality change inspired from [27]. A dimension increase corresponds to a channel coding whereas a decrease corresponds to a source coding.…”

Section: Introductionmentioning

confidence: 99%

Joint Source-Channel Coding Using Real BCH Codes for Robust Image Transmission

Gabay

Kieffer

Duhamel

2007

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

In this paper, a new still image coding scheme is presented. In contrast with standard tandem coding schemes, where the redundancy is introduced after source coding, it is introduced before source coding using real BCH codes. A joint channel model is first presented. The model corresponds to a memoryless mixture of Gaussian and Bernoulli-Gaussian noise. It may represent the source coder, the channel coder, the physical channel, and their corresponding decoder. Decoding algorithms are derived from this channel model and compared to a state-of-art real BCH decoding scheme. A further comparison with two reference tandem coding schemes and the proposed joint coding scheme for the robust transmission of still images has been presented. When the tandem scheme is not accurately tuned, the joint coding scheme outperforms the tandem scheme in all situations. Compared to a tandem scheme well-tuned for a given channel situation, the joint coding scheme shows an increased robustness as the channel conditions worsen.

show abstract

Using 2:1 Shannon Mapping for Joint Source-Channel Coding

Cited by 69 publications

References 5 publications

Enhancing the Protection of Digital Images against Tampering Using Joint Source Channel Coding

Enhancing the Protection of Digital Images against Tampering Using Joint Source Channel Coding

Hybrid digital-analog coding with bandwidth compression for gaussian source-channel pairs

Joint Source-Channel Coding Using Real BCH Codes for Robust Image Transmission

Contact Info

Product

Resources

About