Effects of the Eencoding Scheme on the Perceived Compressed Video Quality Transmitted Over Lossy IP Networks

2006 ◽  
Author(s):  
Ron Shmueli ◽  
Ofer Hadar ◽  
Revital Huber ◽  
Masha Maltz ◽  
Merav Huber
Keyword(s):  
Video Quality ◽  
Ip Networks ◽  
Compressed Video

An Improved Error Concealment Based on Redundant Motion Vector Information

2012 ◽  
Vol 532-533 ◽  
pp. 1219-1224
Author(s):  
Hong Tao Deng
Keyword(s):  
Video Transmission ◽  
Error Concealment ◽  
Video Quality ◽  
Motion Vector ◽  
Compressed Video ◽  
Vector Information ◽  
Vector Estimation ◽  
Channel Errors ◽  
Bit Stream

During video transmission over error prone network, compressed video bit-stream is sensitive to channel errors that may degrade the decoded pictures severely. In order to solve this problem, error concealment technique is a useful post-processing tool for recovering the lost information. In these methods, how to estimate the lost motion vector correctly is important for the quality of decoded picture. In order to recover the lost motion vector, an Decoder Motion Vector Estimation (DMVE) criterion was proposed and have well effect for recover the lost blocks. In this paper, we propose an improved error concealment method based on DMVE, which exploits the accurate motion vector by using redundant motion vector information. The experimental results with an H.264 codec show that our method improves both subjective and objective decoder reconstructed video quality, especially for sequences of drastic motion.

Quality of Experience vs. QoS in Video Transmission

2010 ◽  
pp. 352-376 ◽  
Author(s):  
André F. Marquet ◽  
Jânio M. Monteiro ◽  
Nuno J. Martins ◽  
Mario S. Nunes
Keyword(s):  
Communication Networks ◽  
Video Transmission ◽  
Quality Of Experience ◽  
Video Quality ◽  
Subjective Assessment ◽  
Ip Networks ◽  
Point Of View ◽  
Qos Parameters ◽  
Quality Video

In legacy television services, user centric metrics have been used for more than twenty years to evaluate video quality. These subjective assessment metrics are usually obtained using a panel of human evaluators in standard defined methods to measure the impairments caused by a diversity of factors of the Human Visual System (HVS), constituting what is also called Quality of Experience (QoE) metrics. As video services move to IP networks, the supporting distribution platforms and the type of receiving terminals is getting more heterogeneous, when compared with classical video distributions. The flexibility introduced by these new architectures is, at the same time, enabling an increment of the transmitted video quality to higher definitions and is supporting the transmission of video to lower capability terminals, like mobile terminals. In IP Networks, while Quality of Service (QoS) metrics have been consistently used for evaluating the quality of a transmission and provide an objective way to measure the reliability of communication networks for various purposes, QoE metrics are emerging as a solution to address the limitations of conventional QoS measuring when evaluating quality from the service and user point of view. In terms of media, compressed video usually constitutes a very interdependent structure degrading in a non-graceful manner when exposed to Binary Erasure Channels (BEC), like the Internet or wireless networks. Accordingly, not only the type of encoder and its major encoding parameters (e.g. transmission rate, image definition or frame rate) contribute to the quality of a received video, but also QoS parameters are usually a cause for different types of decoding artifacts. As a result of this, several worldwide standard entities have been evaluating new metrics for the subjective assessment of video transmission over IP networks. In this chapter we are especially interested in explaining some of the best practices available to monitor, evaluate and assure good levels of QoE in packet oriented networks for rich media applications like high quality video streaming. For such applications, service requirements are relatively loose or difficult to quantify and therefore specific techniques have to be clearly understood and evaluated. By the mid of the chapter the reader should have understood why even networks with excellent QoS parameters might have QoE issues, as QoE is a systemic approach that does not relate solely to QoS but to the ensemble of components composing the communication system.

scholarly journals Low delay error resilience algorithm for H.265|HEVC video transmission

2019 ◽  
Vol 17 (6) ◽  
pp. 2047-2063
Author(s):  
Taha T. Alfaqheri ◽  
Abdul Hamid Sadka
Keyword(s):  
Video Coding ◽  
Video Transmission ◽  
High Efficiency ◽  
Video Quality ◽  
Error Resilience ◽  
Video Codec ◽  
Compressed Video ◽  
High Efficiency Video Coding ◽  
Low Delay ◽  
The Impact

AbstractTransmission of high-resolution compressed video on unreliable transmission channels with time-varying characteristics such as wireless channels can adversely affect the decoded visual quality at the decoder side. This task becomes more challenging when the video codec computational complexity is an essential factor for low delay video transmission. High-efficiency video coding (H.265|HEVC) standard is the most recent video coding standard produced by ITU-T and ISO/IEC organisations. In this paper, a robust error resilience algorithm is proposed to reduce the impact of erroneous H.265|HEVC bitstream on the perceptual video quality at the decoder side. The proposed work takes into consideration the compatibility of the algorithm implementations with and without feedback channel update. The proposed work identifies and locates the frame’s most sensitive areas to errors and encodes them in intra mode. The intra-refresh map is generated at the encoder by utilising a grey projection method. The conducted experimental work includes testing the codec performance with the proposed work in error-free and error-prone conditions. The simulation results demonstrate that the proposed algorithm works effectively at high packet loss rates. These results come at the cost of a slight increase in the encoding bit rate overhead and computational processing time compared with the default HEVC HM16 reference software.

scholarly journals Compressed Video Quality Index Based on Saliency-Aware Artifact Detection

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6429
Author(s):  
Liqun Lin ◽  
Jing Yang ◽  
Zheng Wang ◽  
Liping Zhou ◽  
Weiling Chen ◽  
...  
Keyword(s):  
Saliency Detection ◽  
Video Quality ◽  
Video Stream ◽  
Support Vector ◽  
Compressed Video ◽  
Artifact Detection ◽  
Video Signals ◽  
Reference Information ◽  
Video Saliency ◽  
Video Quality Metric

Video coding technology makes the required storage and transmission bandwidth of video services decrease by reducing the bitrate of the video stream. However, the compressed video signals may involve perceivable information loss, especially when the video is overcompressed. In such cases, the viewers can observe visually annoying artifacts, namely, Perceivable Encoding Artifacts (PEAs), which degrade their perceived video quality. To monitor and measure these PEAs (including blurring, blocking, ringing and color bleeding), we propose an objective video quality metric named Saliency-Aware Artifact Measurement (SAAM) without any reference information. The SAAM metric first introduces video saliency detection to extract interested regions and further splits these regions into a finite number of image patches. For each image patch, the data-driven model is utilized to evaluate intensities of PEAs. Finally, these intensities are fused into an overall metric using Support Vector Regression (SVR). In experiment section, we compared the SAAM metric with other popular video quality metrics on four publicly available databases: LIVE, CSIQ, IVP and FERIT-RTRK. The results reveal the promising quality prediction performance of the SAAM metric, which is superior to most of the popular compressed video quality evaluation models.

scholarly journals VideoSet: A large-scale compressed video quality dataset based on JND measurement

2017 ◽  
Vol 46 ◽  
pp. 292-302 ◽  
Author(s):  
Haiqiang Wang ◽  
Ioannis Katsavounidis ◽  
Jiantong Zhou ◽  
Jeonghoon Park ◽  
Shawmin Lei ◽  
...  
Keyword(s):  
Large Scale ◽  
Video Quality ◽  
Compressed Video
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