Performance analysis and recursive syndrome decoding of DFT codes for bursty erasure recovery

Rath, Gagan; Guillemot, Christine

doi:10.1109/tsp.2003.810286

Cited by 34 publications

(22 citation statements)

References 14 publications

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“…DFT is used to transform original image from spatial to frequency domain [7]- [9]. In image processing, the samples can be the values of pixels along a row or column of an image.…”

Section: Introductionmentioning

confidence: 99%

Image Transmission in Military Network Using Bézier Curve

Rizal¹,

Kim²

2015

JACN

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Abstract-This paper proposed a method for reconstruct image by using Bézier Curve function. One of the causes of errors in image reconstruction is lack of sampling. In order to solve that problem, this paper proposes a method by using Bézier Curve to interpolate data between sampling points to reduce the error. The proposed method is compared to the previous works; compressive sampling and discrete Fourier transform (DFT). Compressive sampling is a method which has small number of data sample and DFT is used to perform Fourier analysis in many practical applications in digital signal processing. The result shows that image reconstruction using Bézier curve has closer quality to the original image compared to compressive sampling and DFT while it can maintain small size of reconstructed images.

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“…DFT is used to transform original image from spatial to frequency domain [7]- [9]. In image processing, the samples can be the values of pixels along a row or column of an image.…”

Section: Introductionmentioning

confidence: 99%

Image Transmission in Military Network Using Bézier Curve

Rizal¹,

Kim²

2015

JACN

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“…Basically, the authors propose to use finite tight frames for R M to transmit data over erasure channels; these are channels over which transmission errors can be modelled in terms of the loss (erasure) of certain packets of data. They show that the redundancy of these frames can be used to 'mitigate the effect of the losses in a packet-based communication system' [3,4]. In References [2,4], it was shown that the discrete Fourier transform (DFT) codes (defined in the complex field C) belong also to the class of tight frames for error correction.…”

Section: Introductionmentioning

confidence: 99%

CGU‐frame‐based representations and their connection with Reed—Solomon and DCT/DST coding schemes

Abdelkefi

2008

Trans Emerging Tel Tech

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SUMMARYWe investigate the use of overcomplete frame representations to correct errors occurring over burst-based transmission channels or channels leading to isolated errors. We show that when the overcomplete signal representation is based on a class of frames, called cyclic geometrically uniform (CGU) finite frames, the family of frames containing finite harmonic frames (both in C M and R M ), this representation becomes equivalent to a Reed-Solomon (RS) coding scheme. Hence, introducing an RS decoding procedure at the receiver, leads to remove the errors introduced by the transmission channel and consequently results in a quasi-perfect reconstructed signal. The advantage of this approach is to exploit the RS coding scheme without using it explicitly at the transmitter, which would lead to a robust and low complexity transmission. Furthermore, we prove that the discrete cosine transform (DCT) coding is a special case of CGU-framebased representations and this property holds also true for the discrete sine transform (DST) coding scheme.Simulation results are presented to confirm our claims.

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“…This technique has been improved using a multiple hypotheses test for estimating the number of impulse errors [4] and by realizing an a posteriori control of the correction technique [3], which are computationally less demanding than the techniques presented in [33]. Further results on real BCH codes have been obtained for code vectors corrupted by bursts of erasures (with known locations) in [36] where the background noise is taken into account. In [37], real BCH codes are embedded in the theory of real-valued frames, which allow a further analysis of the influence of the background noise on erasure recovery.…”

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.

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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.

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Performance analysis and recursive syndrome decoding of DFT codes for bursty erasure recovery

Cited by 34 publications

References 14 publications

Image Transmission in Military Network Using Bézier Curve

Image Transmission in Military Network Using Bézier Curve

CGU‐frame‐based representations and their connection with Reed—Solomon and DCT/DST coding schemes

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

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