scholarly journals Performance of Constant Quality Video Applications using the DCCP Transport Protocol

2006 ◽  
Author(s):  
J. Van Velthoven ◽  
K. Spaey ◽  
C. Blondia
Keyword(s):  
Transport Protocol ◽  
Quality Video ◽  
Constant Quality

scholarly journals Video coding and constant quality evaluation using 4k aomenc-AV1 and rav1e-AV1 formats

2021 ◽  
Vol 18 (2) ◽  
pp. 139-154
Author(s):  
Milan Milivojevic ◽  
Dragi Dujkovic ◽  
Ana Gavrovska
Keyword(s):  
Video Transmission ◽  
Frame Rate ◽  
Constraint Factor ◽  
Comparison Results ◽  
Quality Video ◽  
Constant Quality ◽  
Reference Tools ◽  
Quality Constraint ◽  
High Quality Video ◽  
Efficient Video

It is much expected from the relatively novel, open, royalty-free AV1 (Alliance for Open Media (AOMedia) Video 1) standard. At this moment, there are many new variants of AV1 format. It is designed for efficient video internet delivery and high-quality video transmission. AV1 is recognized as Google?s VP9 format successor. One of the reference tools used so far for testing AV1 is libaom- AV1. Nevertheless, due to its time-consuming performance, there are now different available standalone solutions for experimental analysis. Here, one such solution AOMedia?s standalone aomenc (aomenc-AV1) is tested in order to analyze quality assessment based on constant quality constraint factor. Three different metrics are calculated for various 4k video content of the same frame rate. Moreover, rav1e implementation was tested for the same visual data, where rav1e-AV1 also represents an AV1 video encoder, which is considered reliable and suitable in most cases, where libaom is not applicable. In this paper, the comparison results between aomenc-AV1 and rav1e-AV1 are shown.

Constant quality video coding using video content analysis

2005 ◽  
Vol 20 (4) ◽  
pp. 343-369 ◽  
Author(s):  
Luk Overmeire ◽  
Lode Nachtergaele ◽  
Fabio Verdicchio ◽  
Joeri Barbarien ◽  
Peter Schelkens
Keyword(s):  
Content Analysis ◽  
Video Coding ◽  
Video Content ◽  
Video Content Analysis ◽  
Quality Video ◽  
Constant Quality

scholarly journals Constant frame quality control for H.264/AVC

2013 ◽  
Vol 2 ◽  
Author(s):  
Ching-Yu Wu ◽  
Po-Chyi Su ◽  
Long-Wang Huang ◽  
Chia-Yang Chiou
Keyword(s):  
Quality Control ◽  
Control Mechanism ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Video Frame ◽  
Quality Video ◽  
Q Model ◽  
Constant Quality ◽  
Quality Control Mechanism

A frame quality control mechanism for H.264/AVC is proposed in this research. The research objective is to ensure that a suitable quantization parameter (QP) can be assigned to each frame so that the target quality of each frame will be achieved. One of the potential application is consistently maintaining  frame quality during the encoding process to facilitate video archiving and/or video surveillance. A single-parameter distortion to quantization (D–Q) model is derived by training a large number of frame blocks. The model parameter can be determined from the frame content before the exact encoding process. Given the target quality for a video frame, we can then select an appropriate QP according to the proposed D–Q model. Model refinement and QP adjustment of subsequent frames can be applied by examining the coding results of previous data. Such quality measurements as peak signal to noise ratio (PSNR) and structural similarity (SSIM) can be employed. The experimental results verify the feasibility of the proposed constant quality video coding framework.

Virtualized Fog Network with Load Balancing for IoT based Fog-to-Cloud

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Istabraq M. Al-Joboury ◽  
Emad H. Al-Hemiary
Keyword(s):  
Load Balancing ◽  
Packet Loss ◽  
Fog Computing ◽  
Transport Protocol ◽  
Network Simulator ◽  
Huge Number ◽  
Message Queue ◽  
Limited Bandwidth ◽  
Cloud Architecture ◽  
Fog Network

Fog Computing is a new concept made by Cisco to provide same functionalities of Cloud Computing but near to Things to enhance performance such as reduce delay and response time. Packet loss may occur on single Fog server over a huge number of messages from Things because of several factors like limited bandwidth and capacity of queues in server. In this paper, Internet of Things based Fog-to-Cloud architecture is proposed to solve the problem of packet loss on Fog server using Load Balancing and virtualization. The architecture consists of 5 layers, namely: Things, gateway, Fog, Cloud, and application. Fog layer is virtualized to specified number of Fog servers using Graphical Network Simulator-3 and VirtualBox on local physical server. Server Load Balancing router is configured to distribute the huge traffic in Weighted Round Robin technique using Message Queue Telemetry Transport protocol. Then, maximum message from Fog layer are selected and sent to Cloud layer and the rest of messages are deleted within 1 hour using our proposed Data-in-Motion technique for storage, processing, and monitoring of messages. Thus, improving the performance of the Fog layer for storage and processing of messages, as well as reducing the packet loss to half and increasing throughput to 4 times than using single Fog server.

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