Recent trends in online mutimedia education for heterogeneous end-user devices based on Scalable Video Coding

2013 ◽  
Author(s):  
D. Grois ◽  
O. Hadar
Keyword(s):  
Video Coding ◽  
Scalable Video Coding ◽  
Scalable Video ◽  
End User ◽  
Recent Trends

Region-of-Interest Processing and Coding Techniques

2013 ◽  
pp. 126-155 ◽  
Author(s):  
Dan Grois ◽  
Ofer Hadar
Keyword(s):  
Video Coding ◽  
Cell Phones ◽  
Scalable Video Coding ◽  
Region Of Interest ◽  
Scalable Video ◽  
Video Adaptation ◽  
End User ◽  
High Definition ◽  
Processing Power ◽  
Video Model

This chapter comprehensively covers the topic of the Region-of-Interest (ROI) processing and coding for multimedia applications. The variety of end-user devices with different capabilities, ranging from cell phones with small screens and restricted processing power to high-end PCs with high-definition displays, have stimulated significant interest in effective technologies for video adaptation. Therefore, the authors make a special emphasis on the ROI processing and coding with regard to the relatively new H.264/SVC (Scalable Video Coding) standard, which have introduced various scalability domains, such as spatial, temporal, and fidelity (SNR/quality) domains. The authors’ observations and conclusions are supported by a variety of experimental results, which are compared to the conventional Joint Scalable Video Model (JSVM).

Recent Advances in Peer-to-Peer Video Streaming by Using Scalable Video Coding

2012 ◽  
pp. 162-195 ◽  
Author(s):  
Dan Grois ◽  
Ofer Hadar
Keyword(s):  
Video Coding ◽  
Video Streaming ◽  
Scalable Video Coding ◽  
Peer To Peer ◽  
Peer Networks ◽  
Scalable Video ◽  
End User ◽  
Coding Efficiency ◽  
Peer To Peer Networks ◽  
The Media

Scalable Video Coding provides important functionalities, such as the spatial, temporal, and SNR (quality) scalability, thereby significantly improving coding efficiency over prior standards such as the H.264/AVC and enabling the power adaptation. In turn, these functionalities lead to the enhancement of the video streaming over Peer-to-Peer networks, thereby providing a powerful platform for a variety of multimedia streaming applications, such as video-on-demand, video conferencing, live broadcasting, and many others. P2P systems are considered to be extremely cost-effective, since they utilize resources of the peer machines (e.g., CPU resources, memory resources, and bandwidth). However, since bandwidth is usually not constant and also since Peer-to-Peer networks suffer from the packet loss, there is no guarantee for the end-user video presentation quality. In addition, due to different server and end-user hardware configurations, it will be useful to specify the quality of the media (e.g., the bit-rate, spatial/temporal resolution, and the like). As a result, the Scalable Video Coding approach is an excellent choice, since the media streaming can be adjusted to a suitable stream to fit a particular Peer-to-Peer network and particular end-user requirements.

Efficient Scalable Video Coding Method Using Discrete Bandelet Transform

2020 ◽  
Vol 34 (14) ◽  
pp. 2055023
Author(s):  
Yogananda Patnaik ◽  
Dipti Patra
Keyword(s):  
Video Coding ◽  
Scalable Video Coding ◽  
Research Area ◽  
Set Partitioning ◽  
Scalable Video ◽  
Vector Decomposition ◽  
Coding Method ◽  
Benchmark Datasets ◽  
Spatio Temporal ◽  
Ordered Data

Video coding is an imperative part of the modern day communication system. Furthermore, it has vital roles in the fields of video streaming, multimedia, video conferencing and much more. Scalable Video Coding (SVC) is an emerging research area, due to its extensive application in most of the multimedia devices as well as public demand. The proposed coding technique is capable of eliminating the Spatio-temporal regularity of a video sequence. In Discrete Bandelet Transform (DBT), the directions are modeled by a three-directional vector field, known as structural flow. Regularity is decided by this flow where the data entropy is low. The wavelet vector decomposition of geometrically ordered data results in a lesser extent of significant coefficients. The directions of geometrical regularity are interpreted with a two-dimensional vector, and the approximation of these directions is found with spline functions. This paper deals with a novel SVC technique by exploiting the DBT. The bandelet coefficients are further encoded by utilizing Set Partitioning in Hierarchical Trees (SPIHT) encoder, followed by global thresholding mechanism. The proposed method is verified with several benchmark datasets using the performance measures which gives enhanced performance. Thus, the experimental results bring out the superiority of the proposed technique over the state-of-arts.

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