Real-time multimedia coding and transmission

This paper sums up relevant topics covered by the special issue titled ‘Real-Time Multimedia Coding and Transmission’, including efficient content representation, multimedia transmission, hardware and software acceleration, and transcoding techniques.


Specific advances
In this special issue, we have covered fundamental aspects related to image and video multimedia coding and transmission techniques with special emphasis on those techniques suitable for resource-constrained devices and applications with real-time temporal restrictions. including the data transmission format for the underground mud pulse signal. MWD is a well-known technique in oil and gas exploration and consists of providing data measurements to guide the drilling process similarly to the data provided to a pilot by flight instrumentation. In this work, the authors propose a set of algorithms to reduce noise and interference disrupting the so-called mud pulse signals, i.e., the wireless signals employed for communication between the sensors located close to the drilling machinery and the so-called ground-collection box, which is in charge of processing all received data. The proposed algorithms were tested satisfactorily by means of simulations and also in an oil field in the north of China.
In addition to channel and network optimization techniques for multimedia transmission, both hardware and software acceleration techniques for multimedia coding have also been covered in this special issue. This topic is discussed in the following articles: • The authors Vicente Galiano, Otoniel López, Manuel P. Malumbres, and Héctor Migallón in the article entitled 'Multicore-based 3D-DWT Video Encoder' present a three-dimensional discrete wavelet transform (3D-DWT) video encoder based on a fast run-length coding engine. Furthermore, the authors introduce several multi-core optimizations to speed up the 3D-DWT computation. Results show that the proposed encoder obtains good rate/distortion results for high-resolution video sequences with nearly in-place computation using only the memory needed to store a group of pictures while being able to compress a full HD video sequence in real time.

• In the article entitled 'Multi-GPU based on
Multicriteria Optimization for Motion Estimation System' by Carlos Garcia, Guillermo Botella, Fermin Ayuso, Manuel Prieto, and Francisco Tirado, the authors present a graphics processing unit (GPU)-based version of a neuromorphic motion estimation algorithm with low memory consumption. An evolutionary algorithm was used to find the best configuration, which is a trade-off solution between consumption of resources, parallel efficiency, and accuracy. Both a grain level (by means of multi-GPU systems) and a finer level (by data parallelism) have been exploited in the proposed solution. • Finally, Kuang-Shyr Wu in the article entitled 'A Secret Image Sharing Scheme for Light Images' presents a new (r, n)-threshold secret image sharing scheme with low information overhead for images having a low distortion rate, being more applicable for light images. In the proposed solution, a secret image is encoded into n noise-like shadow images to satisfy the condition that any r of the n shadow images (also denoted as shares) can be used to reveal the secret image, whereas no information on the secret can be revealed from any r − 1 or fewer shadow images. The proposed method reduces the size of the shadow images for further storage or transmission.
With respect to the intelligent optimization for transcoding topic, it has been covered by the following articles: • In the article entitled 'Region-of-Interest Determination and Bit-rate Conversion for H.264 Transcoding' by Shu-Fen Huang, Mei-Juan Chen, Kuang-Han Tai, and Mian-Shiuan Li, the authors present a video bit-rate transcoder for the baseline http://asp.eurasipjournals.com/content/2013/1/114 profile in H.264/AVC. The objective is to fit the available channel bandwidth for the client when transmitting video bit streams via communication channels. In order to maintain visual quality for low bit-rate video, they analyze the decoded information in the transcoder and propose a Bayesian-based region-of-interest (ROI) determination algorithm in such a way that the transcoded video will conform to the target bit rate by re-quantization according to those models. The ROI-based transcoder allocates more coding bits to ROI regions and reduces the complexity of the re-encoding procedure for non-ROI regions, not only keeping the coding quality, but also improving transcoding efficiency, thus making real-time transcoding more practical.

• In the article entitled 'Temporal Scalable Mobile
Video Communications based on an Improved WZ-to-SVC transcoder' by Alberto Corrales-García, José Luis Martínez, Gerardo Fernández-Escribano, and Francisco José Quiles, the authors present a WynerZiv (WZ) coding approach to SVC transcoding. Applications such as low-power sensor networks, video surveillance cameras, or mobile communications present a different framework in which low-cost senders transmit video bit streams to a central receiver. In order to manage this kind of application efficiently, WZ coding proposes a solution in which most of the complexity is moved from the encoder to the decoder. Despite the advantages of the video transcoding framework, the transcoder accumulates high complexity and must be reduced in order to avoid excessive delays in communication. Thus, in order to reduce that delay, the information generated during the WZ stage is reused during the SVC stage. Consequently, the time taken by the transcoding is reduced by around 77.77%, with a negligible rate-distortion penalty. • Finally, Rosario Garrido-Cantos, Jan De Cock, José Luis Martínez, Sebastian Van Leuven, Pedro Cuenca, and Antonio Garrido in the article entitled 'Low Complexity Transcoding Algorithm from H.264/AVC to-SVC using Data Mining' propose a low-complexity algorithm to convert an H.264/AVC bit stream without scalability to scalable bit streams with temporal scalability in the baseline and main profiles by accelerating the mode decision task of the SVC encoding stage using machine learning tools. When these techniques are applied, the complexity is reduced by 87% while maintaining coding efficiency.