2012
DOI: 10.1109/tcsvt.2011.2177143
|View full text |Cite
|
Sign up to set email alerts
|

Modeling of Rate and Perceptual Quality of Compressed Video as Functions of Frame Rate and Quantization Stepsize and Its Applications

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
96
0
3

Year Published

2014
2014
2021
2021

Publication Types

Select...
6
2
1

Relationship

0
9

Authors

Journals

citations
Cited by 97 publications
(100 citation statements)
references
References 9 publications
1
96
0
3
Order By: Relevance
“…Since talking head videos are used in this study and the VQD system is dealing with the videos frame by frame before passing the frames to the codec, the value of b was approximately 1. However, according to other studies with diverse video content, b has been found to vary with the intensity of motion [17]. The parameter d had the value of 0.6312.…”
Section: Bit-rate Modelmentioning
confidence: 84%
“…Since talking head videos are used in this study and the VQD system is dealing with the videos frame by frame before passing the frames to the codec, the value of b was approximately 1. However, according to other studies with diverse video content, b has been found to vary with the intensity of motion [17]. The parameter d had the value of 0.6312.…”
Section: Bit-rate Modelmentioning
confidence: 84%
“…For the parametric modeling of MOS (i.e., video quality or QoE) with respect to SVC scalability (t and QP ), following [35], [36], we define perceptive video quality, Q(q, t) that approximates the MOS, where q = 2 (QP −4)/6 . Specifically, as noted in [35], the parametric quality measure has a direct relationship with the subjective measure MOS, given by equation (7): MOS = 4 × Q(q, t) + 1…”
Section: B Subjective Video Quality Assessment Results and Parametrimentioning
confidence: 99%
“…To this end, in the following, we introduce the Quality of Experience (QoE) as an additional requirement in the eMBMS system design. We use recently proposed QoE models for H.264/SVC services that provide analytical relations between the average data rates, R(q, t), and average subjective video quality based on Mean Opinion Score (MOS), Q(q, t), as a function of major H.264/SVC parameters: the frame rate (t) (Hz) and the quantization stepsize parameter (q) [30]. In this paper, we choose the rate and quality model parameters and the encoder settings, that are provided in [30] for the Foreman video sequence encoded using the SVC reference software [32].…”
Section: Qoe-aware Embms Designmentioning
confidence: 99%
“…We use recently proposed QoE models for H.264/SVC services that provide analytical relations between the average data rates, R(q, t), and average subjective video quality based on Mean Opinion Score (MOS), Q(q, t), as a function of major H.264/SVC parameters: the frame rate (t) (Hz) and the quantization stepsize parameter (q) [30]. In this paper, we choose the rate and quality model parameters and the encoder settings, that are provided in [30] for the Foreman video sequence encoded using the SVC reference software [32]. Using the QoE models, we reformulate the eMBMS design problem and analyze the bandwidth and energy efficiency per unit of service in a way which is natural for mobile network operators: What are the bandwidth/energy costs per unit of eMBMS service satisfying certain average video quality threshold Q th for a given eMBMS system configuration?…”
Section: Qoe-aware Embms Designmentioning
confidence: 99%