What is the assumption that information packets are protected more heavily than later packets?

Initial information packets are protected more heavily than later packets, even though every packet contains one unit of reward, since a packet can only be used if all earlier information packets are correctly received.

Why do the integrals simplify to sums?

The integrals typically simplify to sums due to the discrete nature of the problem, and for certain performance measures such as the one based on available source bits, the integrand simplifies due to the constant reward for each bit.

What is the order for performance at all intermediate transmission rates?

The optimality is only guaranteed at certain transmission rates (equal to the cumulative sum of blocklengths starting from the last codeword), so other transmission orders may be better at other intermediate transmission rates;

What is the way to use information bits for erasure?

For the erasure channel, information bits can be immediately used by the source decoder up to the first erasure since any data that arrives on this channel is known to be good.

What is the difference between the two coding methods?

As can be seen in the graph, the optimized schedule strikes a balance between performance at low and high transmission rates relative to the fixed coding methods.

What is the purpose of performance based on available source bits?

Performance based on available source bits assumes all bits as equally important but it allows simplifications in the optimization and eliminates overhead information.

What is the probability of block decoding error for codeword?

A “reward-to-go” function associated with codeword is defined as(3)where is the probability of block decoding error for codeword .

Open AccessProceedings Article10.1109/WCNC.1999.796817

Channel code blocklength and rate optimization for progressive image transmission

P.G. Sherwood, +2 more

- 21 Sep 1999

- Vol. 2, pp 978-982

TL;DR: A general performance measure is proposed for evaluating progressive transmission and dynamic programming is used to determine the channel code parameters based on the progressive performance.

Abstract: We investigate the problem of selecting blocklength and code rate for progressive image transmission, motivated by turbo coding methods where the performance improves with blocklength. The problem is to balance the tradeoff among error protection, source coding rate, and delay. We propose a general performance measure for evaluating progressive transmission and use dynamic programming to determine the channel code parameters based on the progressive performance. Performance results are provided for the evaluation of the gains over less complex methods as a function of channel error rate.

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Most frequently asked questions

1. What are the contributions mentioned in the paper "Channel code blocklength and rate optimization for progressive image transmission" ?

The authors investigate the problem of selecting blocklength and code rate for progressive image transmission, motivated by turbo coding methods where performance improves with blocklength.. The authors propose a general performance measure for evaluating progressive transmission and use dynamic programming to determine the channel code parameters based on the progressive performance.

2. What are the future works in "Channel code blocklength and rate optimization for progressive image transmission" ?

Future work will extend the investigation with turbo codes and include comparisons with low density parity check codes.

3. What is the goal of the optimization problem?

Given a transmission rate constraint , the goal of the optimization problem is to determine the code schedule,, subject tothat maximizes .

4. What is the effect of decoding on the codeword?

The effect is that decoding terminates at the first uncorrectable (and detected) error so no reward is accumulated from subsequent correctly received codewords.

Fig. 4. Performance results for the 512x512 Lena image using turbo codes

Fig. 2. Three possible progressive performance measures each equal to the area of the shaded region.

Fig. 3. Performance results for the 512x512 Lena image transmitted over an erasure channel.

Fig. 1. Distortion vs. rate with and without channel coding.

Citations

Journal Article•10.1109/97.1001646

Robust image transmission using JPEG2000 and turbo-codes

B.A. Banister, +2 more

- 07 Aug 2002

- IEEE Signal Processing Letters

TL;DR: A method of combined source and channel coding is described that provides robustness to errors from a binary symmetric channel and uses the JPEG2000 (JP2) image compression standard.

...read moreread less

170

•Journal Article•10.1109/TCOMM.2003.809256

Source-channel rate allocation for progressive transmission of images

Aria Nosratinia, +2 more

- 02 Apr 2003

- IEEE Transactions on Communications

TL;DR: A parametric methodology for rate allocation in progressive source-channel coding is developed, an empirical model of decoded bit-error rate as a function of the channel code rate is investigated.

...read moreread less

135

Journal Article•10.1109/MSP.2005.1550192

Optimized error protection of scalable image bit streams [advances in joint source-channel coding for images]

Raouf Hamzaoui, +2 more

- 05 Dec 2005

- IEEE Signal Processing Magazine

TL;DR: This article focuses on FEC for scalable image coders, and considers JSCC (joint source-channel coding) at the application layer only, which can be readily extended to transmit scalable compressed bit streams of video sequences and 3-D meshes.

...read moreread less

107

Proceedings Article•10.1109/ICIP.2000.900973

Robust image transmission using JPEG2000 and turbo-codes

B.A. Banister, +2 more

- 10 Sep 2000

TL;DR: A method of combined source and channel coding is described that provides robustness to errors from a binary symmetric channel and uses the JPEG2000 (JP2) image compression standard.

...read moreread less

•Journal Article•10.1109/TCOMM.2003.819235

Fast algorithm for rate-based optimal error protection of embedded codes

Vladimir Stankovic, +2 more

- 17 Nov 2003

TL;DR: An algorithm is given that accelerates the computation of the optimal strategy of Chande and Farvardin by finding an explicit formula for the number of occurrences of the same channel code.

...read moreread less

...

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References

Journal Article•10.1109/76.499834

A new, fast, and efficient image codec based on set partitioning in hierarchical trees

Amir Said, +1 more

- 01 Jun 1996

- IEEE Transactions on Circuits and System...

TL;DR: The image coding results, calculated from actual file sizes and images reconstructed by the decoding algorithm, are either comparable to or surpass previous results obtained through much more sophisticated and computationally complex methods.

...read moreread less

6.1K

•Book

Dynamic Programming: Deterministic and Stochastic Models

Dimitri P. Bertsekas

- 01 Apr 1987

TL;DR: As one of the part of book categories, dynamic programming deterministic and stochastic models always becomes the most wanted book.

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1.5K

Journal Article•10.1109/97.596882

Progressive image coding for noisy channels

P.G. Sherwood, +1 more

- 01 Jul 1997

- IEEE Signal Processing Letters

TL;DR: A progressive image compression scheme whose performance on a noisy channel is significantly better than that of previously known techniques and effectively no degradation due to channel noise can be detected.

...read moreread less

362

Journal Article•10.1109/26.737389

Error protection for progressive image transmission over memoryless and fading channels

P.G. Sherwood, +1 more

- 01 Dec 1998

- IEEE Transactions on Communications

TL;DR: Product channel codes are proposed to protect progressively compressed and packetized images for noisy channels and outperforms the best known image coders for memoryless channels and performs well on fading channels.

...read moreread less

204

Proceedings Article•10.1109/DCC.1999.755658

Graceful degradation over packet erasure channels through forward error correction

A.E. Mohr, +2 more

- 29 Mar 1999

TL;DR: An algorithm that assigns unequal amounts of forward error correction to progressive data so as to provide graceful degradation as packet losses increase is presented, finding that for an exponential packet loss model, good image quality can be obtained, even when 40% of transmitted packets are lost.

...read moreread less

112

Channel code blocklength and rate optimization for progressive image transmission

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Most frequently asked questions

1. What are the contributions mentioned in the paper "Channel code blocklength and rate optimization for progressive image transmission" ?

2. What are the future works in "Channel code blocklength and rate optimization for progressive image transmission" ?

3. What is the goal of the optimization problem?

4. What is the effect of decoding on the codeword?

5. What is the assumption that information packets are protected more heavily than later packets?

6. Why do the integrals simplify to sums?

7. What is the order for performance at all intermediate transmission rates?

8. What is the way to use information bits for erasure?

9. What is the difference between the two coding methods?

10. What is the purpose of performance based on available source bits?

11. What is the probability of block decoding error for codeword?

Figures

Citations

Robust image transmission using JPEG2000 and turbo-codes

Source-channel rate allocation for progressive transmission of images

Optimized error protection of scalable image bit streams [advances in joint source-channel coding for images]

Robust image transmission using JPEG2000 and turbo-codes

Fast algorithm for rate-based optimal error protection of embedded codes

References

A new, fast, and efficient image codec based on set partitioning in hierarchical trees

Dynamic Programming: Deterministic and Stochastic Models

Progressive image coding for noisy channels

Error protection for progressive image transmission over memoryless and fading channels

Graceful degradation over packet erasure channels through forward error correction

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A new, fast, and efficient image codec based on set partitioning in hierarchical trees

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