**4. Summary**

CS is a new field and its application to video systems is even more recent. There are many avenues for further research and thorough quantitative analyses are still lacking. Key encoding strategies adopted so far includes:


Similarly, key decoding strategies includes:


These observations suggest that there are many different approaches to encode videos using CS. In order to achieve a simple encoder design, conventional MPEG type of encoding process should not be adopted. Otherwise, there is no point in using CS as an extra overhead. We believe that the distributed approach in which each key-frame and non-keyframe is encoded by CS is able to utilise CS more effectively. While spatial domain compression is performed by CS, temporal domain compression is not exploited fully since there is no motion compensation and estimation performed. Therefore, a simple but effective inter-frame compression will need to be devised. In the distributed approach, this is equivalent to generating effective side information for the non-key frames.

## **5. References**

12 Video Compression

including the nearest 8 blocks overlapping this block and this block itself are extracted. After that, the K-SVD algorithm [35] is applied to ܳ training patches to learn the dictionary ܦ௧ אܴே್ൈ, ܰ ܲǤ ܦ௧ is an overcomplete dictionary containing ܲ atoms. By using the learned dictionary ܦ௧, each block ܾ௧ in ݔ௧ can be sparsely represented as a sparse coefficient vector ߙ௧ א ܴൈଵ. This learned dictionary provides sparser representation for the frame than using the fixed basis dictionary. Same authors have extended their work in [36] for dynamic measurement rate allocation by incorporating feedback channel in their dictionary based

CS is a new field and its application to video systems is even more recent. There are many avenues for further research and thorough quantitative analyses are still lacking. Key

• Applying CS measurements to all frames (both key frames and non-key frames) as

• Applying conventional coding schemes (MPEG/H.264) to key frames and acquire local block-based and global frame-based CS measurements for non-key frames as suggested

• Split frames into non-overlapping blocks of equal size. Reference frames are sampled fully. After sampling, a compressive sampling test is carried out to identify which

• Reconstructing the key frames by applying CS recovery algorithms such as GPSR and reconstruct the non-key frames by incorporating side information generated by

• Decoding key frames using conventional image or video decompression algorithms and perform sparse recovery with decoder side information for prediction error reconstruction. Add reconstructed prediction error to the block-based prediction frame

• Using a dictionary for decoding [32] where a dictionary is used for comparison and prediction of non-key frames. Similarly, a dictionary can be learned from neighboring

These observations suggest that there are many different approaches to encode videos using CS. In order to achieve a simple encoder design, conventional MPEG type of encoding

Fig. 5. Distributed Compressed Video Sensing with Dictionary Learning

distributed video codec.

suggested by [24].

blocks are sparse [16].

recovered key frames [24].

in [23, 32].

encoding strategies adopted so far includes:

Similarly, key decoding strategies includes:

for final frame reconstruction [23].

frames for reconstruction of non-key frames [33].

**4. Summary** 


**2** 

*Spain* 

**Mobile Video Communications Based** 

Alberto Corrales Garcia1, Gerardo Fernandez Escribano1,

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

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

the transcoding process must be executed as efficiently as possible.

**1. Introduction** 

**on Fast DVC to H.264 Transcoding** 

Jose Luis Martinez2 and Francisco Jose Quiles1 *1Instituto de Investigación en Informática de Albacete,* 

*2Architecture and Technology of Computing Systems Group,* 

*University of Castilla-La Mancha Albacete,* 

*Complutense University, Madrid,* 

