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# Efficient bit rate control method for distributed video coding system

- Chang Woo Lee
^{1}Email author

**2012**:244

https://doi.org/10.1186/1687-6180-2012-244

© Lee; licensee Springer. 2012

**Received:**10 May 2012**Accepted:**18 September 2012**Published:**22 November 2012

## Abstract

An efficient bit rate control method for the transform domain distributed video coding (DVC) system is proposed. In order to decide quantization levels of each transform coefficient in the proposed distributed video decoder, a new bitplanewise zigzag scanning method is used. The bit rate can be controlled precisely in the proposed system, since the number of available bit rates is equal to the number of bitplanes. On the other hand, the bit rate is controlled by changing fixed quantization tables in conventional methods. In the proposed DVC system, Wyner-Ziv frames can be efficiently reconstructed by refining the side information with transmitted parity bits. If there is no transmitted parity bit, the side information is not refined and it is considered to be a decoded Wyner-Ziv frame. The side information is refined more precisely, as the amount of transmitted parity bits increases. The proposed DVC system provides superior coding performance with a precise bit rate control compared to conventional methods.

## Keywords

- Distributed video coding
- Side information
- Wyner-Ziv frame
- Efficient bit rate control
- Bitplanewise zigzag scanning

## Introduction

Efficient compression of video data is essential for storage and communication, since the amount of video data is very large. Video coding standards, such as MPEG or H.264, have been widely used to compress video data. The temporal and spatial correlations of video data are used by adopting the motion compensated prediction and discrete cosine transform (DCT) in the encoder of conventional video coding systems. The conventional video encoder is more complex than the decoder is, since motion compensated prediction requires many operations. This conventional video coding system is appropriate for systems, in which video data is encoded by one complex encoder and decoded by many simple decoders.

A new video coding technique termed distributed video coding (DVC) has been proposed [1–18]. It is based on the Slepian-Wolf and Wyner-Ziv theorems. The Slepian-Wolf theorem says that the minimum rate for encoding two correlated sources separately and decoding jointly is the same as the minimum rate for joint encoding [19]. Wyner and Ziv studied a particular case of Slepian-Wolf coding corresponding to the lossy source coding [20]. In DVC systems, the complexity of encoders is greatly reduced by removing motion estimation operations in the encoder, since the correlation between frames is utilized in decoders [1]. The DVC system is appropriate for emerging applications, such as wireless low-power video surveillance systems, visual sensor networks and mobile systems with ultra light encoders [2, 3]. The transform domain DVC coding system named Power-efficient, Robust, hIgh compression Syndrome based Multimedia coding (PRISM) has been proposed [4]. In this system, the low frequency coefficients are compressed using a trellis-based syndrome Slepian-Wolf code, and the high frequency coefficients are entropy coded. While this system provides good coding performance, the encoding complexity is high. The most popular DVC system was proposed by Aaron et al. at Stanford university [5]. In this system, the input frames in encoders are divided into key frames and Wyner-Ziv frames. While key frames are encoded using intra-frame coding techniques, such as H.264 intracoding technique, Wyner-Ziv frames are encoded with channel encoders such as turbo codes or LDPC codes, and only parity bits are transmitted for Wyner-Ziv frames. In the decoder, the side information, which is an estimate of the original Wyner-Ziv frame, is obtained using key frames. Motion compensated interpolation techniques are usually used to obtain side information. Wyner-Ziv frames can be decoded with the side information and transmitted parity bits, since the side information can be considered to be a noisy version of the original Wyner-Ziv frame. Wyner-Ziv frames in conventional distributed video decoders are reconstructed in the dequantization process, using the side information and transmitted parity bits. If the parity bits are not enough, Wyner-Ziv frames can’t be decoded successfully. On the contrary, sending too much parity bits results in bit rate overhead. Thus, the feedback channel is usually used, since the amount of parity bits is not known in the encoder. The transmission of parity bits is requested through the feedback channel, until the errors are corrected to decode Wyner-Ziv frames. To eliminate the feedback channel, the amount of parity bits should be calculated in the encoder. Brites et al. proposed a simple side information generation technique and encoder rate control method by using the entropy and relative error probabilities [6]. However, the coding performance for the systems without feedback channels degrades due to the mismatch between the estimated and real bit rates [6–8]. Recently, a method for constraining the number of feedback requests to a fixed maximum number of *N* requests was proposed [9].

In this paper, we propose an efficient bit rate control method for the transform domain DVC system. A new bitplanewise zigzag scanning method to decide quantization levels of each transform coefficient is proposed to maximize the rate distortion performance. The different bit rates in the proposed bitplanewise zigzag scanning method are obtained at each scan of the bitplanes. While the number of available bit rates in the conventional DVC systems is seven or eight, which is the number of fixed quantization tables, the quantization table can be easily generated at each scan in the proposed system. The bit rate can be controlled more precisely in the proposed DVC system, since the number of available quantization tables is about eight times greater than that for conventional systems. In the proposed DVC system, the side information is refined with transmitted parity bits, in which the side information refined with transmitted parity bits is considered to be the decoded Wyner-Ziv frame. If no parity bit is transmitted, the side information is not refined and it becomes the reconstructed Wyner-Ziv frame. As the amount of parity bits increases, the quality of decoded Wyner-Ziv frames improves by refining the side information more precisely with parity bits. The proposed decoding method provides superior performance to conventional methods, especially at low bit rates, since the side information can be refined with a small number of parity bits. Computer simulation results show that the proposed decoding and bit rate control method provides superior coding performance and finer bit rate control than the conventional method. While conventional DVC systems usually focus on the performance improvement or management of feedback channel [5–17], the proposed DVC system deals with the precise bit rate control method and performance improvement.

In Distributed video coding system, the DVC system is explained. The proposed DVC system and proposed bit rate control method are presented in Proposed DVC system and Proposed bit rate control method, respectively. Performance is evaluated in Performance evaluation. Finally, Conclusions are given in Conclusion.

## Distributed video coding system

*SS*

_{ DC }is the step size for DC coefficients and

*N*

_{ Q_DC }is the number of quantization levels for DC coefficients. The same kind of quantizers for AC coefficients can be used to quantize AC coefficients. If

*Max*

_{ ACn }is the maximum absolute value for the

*n*th AC coefficient, the step size for the

*n*th AC coefficient is given by

*SS*

_{ ACn }is the step size for the

*n*th AC coefficient and

*N*

_{ Q_ACn }is the number of quantization levels for the

*n*th AC coefficient. This quantizer is symmetric with respect to the zero value, as is shown in Figure 3(a). Many coefficients are located near zero value, since the probability density function of AC coefficients is known to be Laplacian. If we use a symmetric quantizer, many parity bits are required to reconstruct the AC coefficients near the zero value. Thus, we can use a nonsymmetric quantizer, depicted in Figure 3(b), in which zero value is included in the quantization interval, to reduce the number of parity bits for the AC coefficients near the zero value. If we use a quantizer with a dead zone around the zero value, as shown in Figure 3(c), we can use fewer parity bits to encode the AC coefficients near the zero value. Performance evaluation gives the performance analysis for each AC quantizer.

*l*and

*u*represent the lower and the upper bounds of the quantizer interval, respectively, to reconstruct the source information

*x*in Wyner-Ziv frames using a side information

*y*.

where $\widehat{x}$ is the reconstructed DCT coefficient.

*q'*is the decoded quantization bin and

*E*(·) is the expectation operator. In Eq. (4), the conditional probability density function

*f*

_{x|y}(·) represents residual statistics between corresponding coefficients in Wyner-Ziv frames and side information; the Laplacian distribution is assumed [14, 15]. The reconstructed DCT coefficient can be obtained using

In Eq. (5), *α* is the Laplacian distribution parameter for each DCT coefficient and Δ is the quantization bin size.

## Proposed DVC system

The side information is refined progressively, as the corresponding bitplanes are reconstructed using transmitted parity bits [18]. This improves the quality of the reconstructed Wyner-Ziv frame. If the optimal reconstruction method given in Eq. (5) is used, the side information is refined more precisely. The AC quantizer with a dead zone, which is shown in Figure 3(c), is used to quantize AC coefficients, since the progressively refinement process can be implemented with the quantizer. This exhibits better performance than the symmetric quantizer shown in Figure 3(a). The nonsymmetric quantizer cannot be used for the progressively refinement process, since it is nonsymmetric with respect to the zero value.

where *u*_{Wyner − Ziv}(*i*, *j*), ${\widehat{u}}_{Wyner-Ziv}{}^{\mathit{\text{Prop.}}}\left(i,j\right)$ and ${\widehat{u}}_{Wyner-Ziv}{}^{\mathit{\text{Conv.}}}\left(i,j\right)$ represent the pixels of the original Wyner-Ziv frame, the reconstructed Wyner-Ziv frame in the proposed decoder and the reconstructed Wyner-Ziv frame in the conventional decoder, respectively. In the case where there is no parity bit for Wyner-Ziv frames, ${\widehat{u}}_{Wyner-Ziv}^{\mathrm{Pr}op.}\left(i,j\right)$ is the side information, while ${\widehat{u}}_{Wyner-Ziv}^{Conv.}\left(i,j\right)$ is zero. As the amount of parity bits increases, ${\widehat{u}}_{Wyner-Ziv}{}^{\mathit{\text{Prop.}}}\left(i,j\right)$ is refined more precisely, while ${\widehat{u}}_{Wyner-Ziv}{}^{\mathit{\text{Conv.}}}\left(i,j\right)$ is reconstructed by the dequantization process. Although the performance of the proposed decoder depends on the quality of the side information, the proposed decoder outperforms the conventional decoder significantly, especially at low bit rates. This is shown in Performance evaluation. The performance of the proposed decoder is much better than that of the conventional decoder, as the side information gets closer to the original Wyner-Ziv frame.

## Proposed bit rate control method

where *M* and *N* are the number of transform coefficients and the number of bitplanes, respectively. *ΔD*_{
j
}(*i*) is the decreased distortion caused by increasing the quantization levels of ith transform coefficients on the jth bitplanes, and *ΔR*_{
j
}(*i*) is the increased rate needed to increase the quantization level. The optimum scanning order can be obtained by searching the transform coefficient i that minimizes the above formula for each bitplane from 1 to N. However, the optimum scanning order differs for each video sequence. Thus, in the decoder, the optimum scanning order cannot be obtained before the decoding process completes.

## Performance evaluation

The conventional DVC systems usually focus on the performance improvement [11–13, 16, 17] or management of feedback channel [6–9]. The proposed DVC system deals with the precise bit rate control method and performance improvement. The proposed system provides the efficient bit rate control method by using the bitplanewise zigzag scanning method. While seven or eight fixed quantization tables are used in the conventional system, the number of available quantization tables in the proposed system is equal to the number of bitplanes. For example, for the seventh quantizer table in the conventional system, the number of available quantization tables in the proposed system is 63, which is the number of bitplanes. Thus, more quantization tables should be made in the proposed system. The increased decoder complexity for making more quantization tables is almost negligible, since they can be easily generated using the bitplanewise zigzag scanning, which is shown in Figure 6 and Figure 7. The decoder complexity for refining side information doesn’t increase compared to the conventional DVC systems, since the same dequantization method as the conventional DVC systems is used for refining. By setting the initial decoded Wyner-Ziv frame as the side information and refining the side information progressively, the proposed system shows better performance than the conventional DVC systems at low bit rates.

## Conclusions

An efficient bit rate control method for the transform domain DVC system was proposed. By adopting the bitplanewise zigzag scanning method to decide the quantization levels for each transform coefficient, the bit rate can be controlled more precisely in the proposed DVC system, since the number of available quantization tables is about eight times greater than that of fixed quantization tables, which is used to control bit rates in conventional DVC systems. Moreover, the proposed system provides superior coding performance by refining the side information using transmitted parity bits to reconstruct Wyner-Ziv frames. The proposed system performs well especially at low bit rates, since the side information can be refined with a small number of parity bits. The side information is refined more precisely with transmitted parity bits. While Wyner-Ziv frames are decoded by dequantizing transform coefficients with side information and parity bits in the conventional DVC system, the Wyner-Ziv frames are reconstructed by refining side information with parity bits in the proposed system. Simulation results show that the proposed DVC system provides better performance than the conventional DVC system with precise bit rate control. Especially, at low bit rates, the coding performance of the proposed DVC system is significantly better than the conventional DVC system and the proposed DVC system provides much better reconstructed images for all test video sequences than the conventional systems.

## Declarations

### Acknowledgement

This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2012–0002935).

## Authors’ Affiliations

## References

- Girod B, Aaron AM, Rebollo-Monedero D: Distributed video coding.
*Proc. IEEE*2005, 93(1):71-83.View ArticleGoogle Scholar - Pereira F:
*Distributed video coding: basics, main solutions and trends*. International Conference on Multimedia and Expo, New York, NY; 2009:1592-1595.Google Scholar - Pereira F, Torres L, Guillemot C, Ebrahimi T, Leonardi R, Klomp S: Distributed video coding: selecting the most promising application scenarios.
*Signal Process: Image Commun*2008, 23(5):339-352. 10.1016/j.image.2008.04.002Google Scholar - Puri R, Majumdar A, Ramchandram K: PRISM: a video coding paradigm with motion estimation at the decoder.
*IEEE Trans. Image Process.*2007, 16(10):2436-2448.MathSciNetView ArticleGoogle Scholar - Aaron A, Rane S, Setton E, Girod B:
*Transform-domain Wyner-Ziv codec for video*. SPIE Visual Communications and Image Processing, San Jose, CA; 2004:520-528.Google Scholar - Brites C, Pereira F:
*Encoder rate control for transform domain Wyner-Ziv video coding*. IEEE International Conference on Image Processing, San Antonio, TX; 2007:II.5-II.8.Google Scholar - Fu C, Kim J:
*Encoder rate control for block-based distributed video coding*. IEEE International Workshop MMSP, Saint Malo; 2010:333-338.Google Scholar - Morbee M, Prades-Nebot J, Pizurica A, Philips W: Rate allocation algorithm for pixel-domain distributed video coding without feedback channel.
*IEEE Int. Conference on Acoust.*2007, I.521-I.524. Speech Signal Process., Honolulu, HIGoogle Scholar - Skorupa J, Deligiannis JN, Lambert P, Munteanu A, Van de Walle R: Distributed video coding with feedback channel constraints.
*IEEE Trans Circuits and Systems for Video Technology*2012, 22(7):1014-1026.View ArticleGoogle Scholar - Artigas X, Ascenso J, Dalai M, Klomp S, Kubasov D, Ouaret M:
*The DISCOVER codec: architectures, techniques and evaluation*. Picture Coding Symposium, Lisbon, Portugal; 2007:1-4.Google Scholar - Brites C, Ascenso J, Pereira F: Improving transform domain Wyner-Ziv video coding performance.
*IEEE Int. Conference on Acoust. Speech Signal Process*2006, II.525-II.528. ToulouseGoogle Scholar - Brites C, Ascenso J, Pedro JQ, Pereira F: Evaluating a feedback channel based transform domain Wyner-Ziv video codec.
*Signal Process: Image Commun*2008, 23(4):269-297. 10.1016/j.image.2008.03.002Google Scholar - Kubasov D, Nayak J, Guillemot C: Optimal reconstruction in Wyner-Ziv video coding with multiple side information.
*IEEE Int. Workshop on Multimedia Signal Process.*2007, 183: 183-186. CreteGoogle Scholar - Brites C, Pereira F: Correlation noise modeling for efficient pixel and transform domain Wyner-Ziv video coding.
*IEEE Trans Circuits and Systems for Video Technology*2008, 18(9):1177-1190.View ArticleGoogle Scholar - Fan X, Au O, Cheung NM: Transform-domain adaptive correlation estimation (TRACE) for Wyner-Ziv video coding.
*IEEE Trans Circuits and Systems for Video Technology*2010, 20(11):1423-1436.View ArticleGoogle Scholar - Martins R, Brites C, Ascenso J, Pereira F: Refining side information for improved transform domain Wyner-Ziv video coding.
*IEEE Trans Circuits and Systems for Video Technology*2009, 19(9):1327-1341.View ArticleGoogle Scholar - Ye S, Ouaret M, Dufaux F, Ebrahimi T: Improved side information generation with iterative decoding and frame interpolation for distributed video coding.
*IEEE International Conference on Image Processing*2008, 2228-2231. San Diego, CAGoogle Scholar - Lee CW: Efficient decoding method for Wyner-Ziv video codec.
*Workshop of Image Processing and Image Understanding*2011, 368-371. Jeju, KoreaGoogle Scholar - Slepian D, Wolf J: Noiseless coding of correlated information sources.
*IEEE Trans. Inf. Theory*1973, 19(4):471-480. 10.1109/TIT.1973.1055037MathSciNetView ArticleMATHGoogle Scholar - Wyner A, Ziv J: The rate-distortion function for source coding with side information at the decoder.
*IEEE Trans. Inf. Theory*1976, 22(1):1-10. 10.1109/TIT.1976.1055508MathSciNetView ArticleMATHGoogle Scholar

## Copyright

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.