Implementation of scalable video coding deblocking filter from high-level SystemC description

2013 ◽  
Author(s):  
Pedro P. Carballo ◽  
Omar Espino ◽  
Romén Neris ◽  
Pedro Hernández-Fernández ◽  
Tomasz M. Szydzik ◽  
...  
Keyword(s):  
Video Coding ◽  
Scalable Video Coding ◽  
Scalable Video ◽  
Deblocking Filter ◽  
High Level

An Adaptive Deblocking Filter for ROI-Based Scalable Video Coding

2007 ◽  
Author(s):  
Zhongkang Lu ◽  
Jinghong Zheng ◽  
Shiqian Wu ◽  
Weisi Lin ◽  
Susanto Rahardja
Keyword(s):  
Video Coding ◽  
Scalable Video Coding ◽  
Scalable Video ◽  
Deblocking Filter

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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