Traffic and Quality Characterization of Single-Layer Video Streams Encoded with the H.264/MPEG-4 Advanced Video Coding Standard and Scalable Video Coding Extension

2008 ◽  
Vol 54 (3) ◽  
pp. 698-718 ◽  
Author(s):  
G. Van der Auwera ◽  
P.T. David ◽  
M. Reisslein
Keyword(s):  
Video Coding ◽  
Scalable Video Coding ◽  
Single Layer ◽  
Scalable Video ◽  
Video Streams ◽  
Advanced Video Coding

scholarly journals Efficient Enhancement for Spatial Scalable Video Coding Transmission

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Mayada Khairy ◽  
Alaa Hamdy ◽  
Amr Elsayed ◽  
Hesham Farouk
Keyword(s):  
Video Coding ◽  
Video Compression ◽  
Scalable Video Coding ◽  
Standard Technique ◽  
Base Layer ◽  
Scalable Video ◽  
Video Streams ◽  
Coding Efficiency ◽  
Advanced Video Coding ◽  
Second Category

Scalable Video Coding (SVC) is an international standard technique for video compression. It is an extension of H.264 Advanced Video Coding (AVC). In the encoding of video streams by SVC, it is suitable to employ the macroblock (MB) mode because it affords superior coding efficiency. However, the exhaustive mode decision technique that is usually used for SVC increases the computational complexity, resulting in a longer encoding time (ET). Many other algorithms were proposed to solve this problem with imperfection of increasing transmission time (TT) across the network. To minimize the ET and TT, this paper introduces four efficient algorithms based on spatial scalability. The algorithms utilize the mode-distribution correlation between the base layer (BL) and enhancement layers (ELs) and interpolation between the EL frames. The proposed algorithms are of two categories. Those of the first category are based on interlayer residual SVC spatial scalability. They employ two methods, namely, interlayer interpolation (ILIP) and the interlayer base mode (ILBM) method, and enable ET and TT savings of up to 69.3% and 83.6%, respectively. The algorithms of the second category are based on full-search SVC spatial scalability. They utilize two methods, namely, full interpolation (FIP) and the full-base mode (FBM) method, and enable ET and TT savings of up to 55.3% and 76.6%, respectively.

scholarly journals A Novel Hybrid Optimization Algorithm for Scalable Video Coding in an SDN

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Li Wang ◽  
Xiaokai Wang
Keyword(s):  
Genetic Algorithm ◽  
Video Coding ◽  
Optimization Algorithm ◽  
Programming Model ◽  
Moving Average ◽  
Scalable Video Coding ◽  
Single Layer ◽  
Hybrid Optimization ◽  
Scalable Video ◽  
Hybrid Optimization Algorithm

Scalable Video Coding (SVC) is a powerful solution to video application over heterogeneous networks and diversified end users. In the recent years, works mostly concentrate on transported layers or path for a single layer in the Software-Defined Network (SDN). This paper proposes the Novel Hybrid Optimization Algorithm for Scalable Video Coding (NHO-SVC) based on Genetic Algorithm to select the layer and path simultaneously. The algorithm uses the 0/1 knapsack programming model to set up the model, predicts the network states by the Autoregressive Integrated Moving Average Model (ARIMA), and then, makes decision based on Genetic Algorithm.

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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