**1. Introduction**

14 Video Compression

[18] J.Y.Park and M. B. Wakin, "A Multiscale Framework for Compressive Sensing of Video,"

[19] A. Secker and D. Taubman, "Lifting-based invertible motion adaptive transform

[20] R. Marcia and R. Willett, "Compressive Coded Aperture Video Reconstruction," in *16th European Signal Processing Conference, EUSIPCO-2008* Lausanne, Switzerland, 2008. [21] J. Slepian and J. Wolf, "Noiseless coding of correlated information sources," *IEEE Transactions on Information Theory*, vol. 19, no. 4, pp. 471–480, Jul. 1973. [22] A. Wyner, "Recent results in the Shannon theory," *IEEE Transactions on Information* 

[23] T. T. Do, C. Yi, D. T. Nguyen, N. Nguyen, G. Lu, and T. D. Tran, "Distributed

[24] K. Li-Wei and L. Chun-Shien, "Distributed compressive video sensing," in *Acoustics,* 

[25] T.T.Do, L.Gan, and T.D.Tran, "Fast and efficient compressive sampling using structural Random Matrices," *To be submitted to IEEE Trans. of Information Theory, 2008,* 2008. [26] T. T. Do, L. Gan, N. Nguyen, and T. D. Tran, "Sparsity adaptive matching pursuit

[27] M. A. T. Figueiredo, R. D. Nowak, and S. J. Wright, "Gradient Projection for Sparse

*Selected Topics in Signal Processing, IEEE Journal of,* vol. 1, pp. 586-597, 2007. [28] L. Gan, T.T.Do, and T.D.Tran, "Fast compressive imaging using scrambled hadamard block ensemble," in *16th Eurpoian Signal Processing Conference*, Lausanne, Switzerland, 2008. [29] J. M. Bioucas-Dias and M. A. T. Figueiredo, "A New TwIST: Two-Step Iterative

[30] T. Blumensath and M. E. Davies, "Gradient Pursuits," *Signal Processing, IEEE* 

 http://www. compression.ru/video/frame\_rate\_conversion/index\_en\_msu.html. [32] J. Prades-Nebot, M. Yi, and T. Huang, "Distributed video coding using compressive sampling," in *Proceedings of Picture Coding Symposium*, Chicago, IL, USA, 6-8 May 2009. [33] Hung-Wei Chen, K. Li-Wei and L. Chun-Shien, "Dictionary Leraning-Based Distributed

[34 ] S. J. Wright, R. D. Nowak, and M. A. T. Figueiredo, "Sparse reconstruction by separable

[35] M. Aharon, M. Elad, and A. M. Bruckstein, "The K-SVD: an algorithm for designing of

[36] Hung-Wei Chen, Li-Wei Kang, and Chun-Shien Lu, "Dynamic Measurement Rate

[31] K. Simonyan and S. Grishin, "AviSynth MSU frame rate conversion filter."

(LIMAT) framework for highly scalable video compression," *Image Processing, IEEE* 

Compressed Video Sensing," in *Information Sciences and Systems, 2009. CISS 2009.* 

*Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on*,

algorithm for practical compressed sensing," in *Aslimore Conference on Signals,* 

Reconstruction: Application to Compressed Sensing and Other Inverse Problems,"

Shrinkage/Thresholding Algorithms for Image Restoration," *Image Processing, IEEE* 

Compressive Video Sensing," in 28th Picture Coding Symposium. PCS 2010*. Dec 8-*

approximation," *IEEE Trans. on Signal Processing*, vol. 57, no. 7, pp. 2479-2493, July 2009.

overcomplete dictionaries for sparse representation," *IEEE Trans. on Signal* 

Allocation for Distributed Compressive Video Sensing," *Proc. IEEE/SPIE Visual Communications and Image Processing (VCIP): special session on Random Projection and* 

Picture Coding Symposium Chicago, Illinois, 2009.

*Transactions on,* vol. 12, pp. 1530-1542, 2003.

*Theory*, vol. 20, no. 1, pp. 2–10, Jan. 1974.

*43rd Annual Conference on*, 2009, pp. 1-2.

*Systems and Computers* Pacific Grove, California, 2008.

*Transactions on,* vol. 16, pp. 2992-3004, 2007.

*Transactions on,* vol. 56, pp. 2370-2382, 2008.

*Processing*, vol. 54, no. 11, pp. 4311-4322, Nov. 2006.

2009, pp. 1169-1172.

*10, 2010* , pp. 210-213.

*Compressive Sensing*, July 2010

Nowadays, mobile devices demand multimedia services such as video communications due to the advances in mobile communications systems (such us 4G) and the integration of video cameras into mobile devices. However, these devices have some limitations of computing power, resources and complexity constraints for performing complex algorithms. For this reason, in order to establish a video communications between mobile devices, it is necessary to use low complex encoding techniques. In traditional video codecs (such as H.264/AVC (ISO/IEC, 2003)) these low complexity requirements have not been met because H.264/AVC is more complex at the encoder side. Then, mobile video communications based on H.264/AVC low complexity imply a penalty in terms of Rate – Distortion (RD). However, Distributed Video Coding (DVC) (Girod et al., 2005), and particularly Wyner-Ziv (WZ) video coding (Aaron et al., 2002), provides a novel video paradigm where the complexity of the encoder is reduced by shifting the complexity of the encoder to the decoder (Brites et al., 2008). Taking into account the benefits of both paradigms, recently WZ to H.26X transcoders have been proposed in the multimedia community to support mobile-to-mobile video communications. The transcoding framework provides a scheme where transmitter and receiver execute lower complexity algorithms and the majority of the computation is moved to the network where the transcoder is allocated. This complexity is thus assumed by a transcoder, which has more resources and no battery limitations. Nevertheless, for real time communications it is necessary to perform this conversion from WZ to H.264/AVC with a short delay, and then the transcoding process must be executed as efficiently as possible.

At this point, this work presents a WZ to H.264/AVC transcoding framework to support mobile-to-mobile video communications. In order to provide a faster transcoding process both paradigms involve in the transcoder (WZ decoding and H.264/AVC encoding) are accelerated. On the one hand, nowadays parallel programming is becoming more important to solve high complexity computation tasks and, as a consequence, the computing market is

Mobile Video Communications Based on Fast DVC to H.264 Transcoding 17

H.264/AVC or MPEG-4 part 10 Advanced Video Coding (AVC) is a compression video standard developed by the ITU-T Video Coding Experts Group (ITU-T VCEG) together with the ISO/IEC Moving Picture Experts Group (MPEG). In fact, both standards are technically

The main purpose of H.264/AVC is to offer a good quality standard able to considerably reduce the output bit rate of the encoded sequences, compared with previous standards, while exhibiting a substantially increasing definition of quality and image. H.264/AVC promises a significant advance compared with the commercial standards currently most in use (MPEG-2 and MPEG-4). For this reason H.264/AVC contains a large amount of compression techniques and innovations compared to previous standards; it allows more compressed video sequences to be obtained and provides greater flexibility for implementing the encoder. Figure 2 shows the block diagram of the H.264/AVC encoder.

The ME is the most time-consuming task in the H.264/AVC encoder. It is a process which removes the temporal redundancy between images, comparing the current one with previous or later images in terms of time (reference images), looking for a pattern that

To improve the encoding efficiency, H.264/AVC allows the use of partitions resulting from dividing the MB in different ways. Greater flexibility for the ME and Motion Compensated (MC) processes and greater motion vector precision give greater reliability to the H.264/AVC encoding process. The ME process is thus carried out many times per each

The DVC framework is based on displacing the complexity from encoders to decoders. However, reducing the complexity of decoders as much as possible is desirable. In traditional feedback-based WZ architectures (Aaron et al., 2002), the rate control is performed at the decoder and is controlled by means of the feedback channel; this is the

partition and sub-partition. This feature is known as variable block size for the ME.

**2.2 H.264/AVC** 

identical (ISO/IEC, 2003).

Fig. 2. H.264/AVC encoder diagram

**3. Related work** 

**3.1 Parallel Wyner-Ziv** 

indicates how the movement is produced inside the sequence.

full of multicore systems, an approach is proposed to execute WZ decoding in a parallel way. On the other hand, at the same time WZ is decoding, some information could be gathered are sent to the H.264/AVC encoder in order to reduce the encoding algorithm complexity. In this work, the search area of the Motion Estimation (ME) process is reduced by means of Motion Vectors (MVs) calculated in the WZ decoding algorithm. In this way, the complexity of the two most complex tasks of this framework (WZ decoding and H.264/AVC encoding) are largely reduced making the transcoding process more efficient.
