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2.721 br
10,000
8.885 br
1,000
P 1 b () =0.1387 e
+ 0.2823 e
1.584 br
10,000 2.125 e
7.8 br
1,000
P 2 b () = 2.154 e
(3)
2.887 br
10,000 1.307 e
9.414 br
1,000
P 3 b () =1.95 e
Q 1 b () = 1.75 br 3
4.327 br 2
10 7
+ 4.19 br
10 4
+ 0.3876
10 10
This way we can describe the relationship between the bit rate and the packet loss
and how they influence in the obtaining of the quality perceived by the users as
shown in Equation ( 4 ).
) l 2 + P 2 br
) = P 1 br
(
(
) l + P 3 br
(
)
f L l , br
(
(4)
l + Q 1 br
(
)
4.3.4 The Impact of Frame Rate
An interesting study in how the quality perception changes as a function of the
frame rate is shown in [29]. This study categorizes the media streams using three
parameters: temporal nature of the data (i.e., soccer match vs. interview), audio
(auditory) and visual content. Based on this categorization the watchability of the
media streams is analyzed for all the possible combinations and making a classifi-
cation based on the perception of the users between 1 and 7 (where 7 is the best
quality).
We extrapolated this study to the case of a video on demand scenario where the
video sequences have a high temporal nature. Considering that 30 fps has the best
quality (i.e., 7) and normalizing the values, we can see the degradation factor that
suffers the media rating in function of its frame rate in Fig. 4.
The equation that describes the curve of degradation of quality in function of
the frame rate and minimizes the mean quadratic error is shown in Equation ( 5 )
where fps is the number of frames per second.
) = 0.00102 fps 2 + 1.164 fps + 1.704
fps + 5.714
]
f R fps
(
fps 5, 30
[
(5)
The curves that describe the different frame rates studied were calculated using an
initial curve obtained - as explained in Section 4.3.2 - and the degradation factor
of Equation ( 5 ) were applied. In order to check the accuracy of the proposed
equation, the different curves have been compared with the data obtained from the
real users as is shown in Fig. 5.
 
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