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Section 2.5. Video Coding Basics 27
the double stimulus impairment scale (DSIS) and the double and single stim-
ulus continuous quality scales, (DSCQS) and (SSCQS), respectively. For a
detailed description of such experiments the reader is referred to Ref. 25.
Despite their reliability, subjective quality experiments are expensive and
time consuming. Objective measures provide cheaper and faster alternatives.
One commonly used objective measure is the mean squared error (MSE),
which is de ned as
H
V
1
ˆ
MSE = [ f(x; y) − f(x; y)] ; (2.18)
2
H × V
x=1 y=1
where H and V are the horizontal and vertical dimensions of the frame, re-
ˆ
spectively, and f(x; y) and f(x; y) are the pel values at location (x; y)of
the original and reconstructed frames, respectively. Care should be taken to
include color components and to take into account any chroma subsampling.
For example, the MSE of a reconstructed 4:2:0 color frame can be calculated
as
V
H
1
ˆ
MSE 4:2:0 = 3 [Y (x; y) − Y (x; y)] 2
2 H × V x=1 y=1
H=2 V=2
ˆ
+ [C (x; y) − C (x; y)] 2
R
R
x=1 y=1 (2.19)
H=2 V=2
ˆ
+ [C (x; y) − C (x; y)] 2
B
B
x=1 y=1
2 1 1 � ):
�
= (MSE Y � + MSE C + MSE C
3 4 R 4 B
A more common form of the MSE measure is the peak signal-to-noise
ratio (PSNR), which is de ned as
2
f
max
PSNR = 10 log 10 MSE (dB); (2.20)
where f max is the maximum possible pel value (for example, 255 for an 8-bit
resolution component). Although this measure does not always correlate well
with perceived video quality, its relative simplicity makes it a very popular
choice in the video coding community. Thus, to facilitate comparisons with
other algorithms reported in the literature, this book adopts the PSNR measure.
If accuracy is a major concern, then more sophisticated objective measures
based on perceptual models can be used [26].
When testing a video coding algorithm, it is very important to subject it to
a range of input video sequences with di6erent characteristics and a reasonable