Appendix A Investigation of Image Quality of Dirac, H.264 and H.265

226 Pages·2014·8.14 MB·English

Appendix A
Investigation of Image Quality of Dirac,
H.264 and H.265
This appendix is based on the project by Biju Shrestha [E44].
For more details, please see Projects (Spring 2012) on UTA’s EE5359 course
website: http://www-ee.uta.edu/Dip/Courses/EE5359/index.html.
A.1 Introduction
Shrestha [E44] has implemented H.265 for QCIF and CIF sequences and
compared with Dirac and H.264. There exist several standards for video
compression with additional improvements in performance and qualities in
comparison to their older versions [H46]. The image quality of Dirac, H.264 and
H.265 can be investigated using metrics like PSNR, CSNR, MSE, SSIM, MS
SSIM,andFSIM[Q13,Q27,Q28]usingvarioustestsequences.Theconventional
metrics like PSNR and MSE are a measure of intensity and cannot measure the
subjectivefidelity[Q16].ThemetricslikeSSIMandFSIMtakeintoaccountofthe
human visual system.
A.2 H.265
H.265 is also known as HEVC [E5] and it can deliver significantly improved
compression performance relative to that of the AVC (ITU-T H.264 | ISO/IEC
14496-10) [E5]. Alshina et al. [E2] investigated the coding efficiency with high
resolution, HD 1080p, and concluded that it can be increased by average 37 and
36 %bitsavingsforhierarchicalBstructureandIPPPstructurewhencomparedto
MPEG-4 AVC [E2]. The typical block-based video codec is composed of many
processes including intra prediction and interprediction, transforms, quantization,
entropy coding, and filtering [E10] as shown in Figs. A.1 and A.2. Over the
decade, video coding techniques have gone through intensive research to achieve
higher coding efficiencies.
K.R.Raoetal.,VideoCodingStandards, 271
SignalsandCommunicationTechnology,DOI:10.1007/978-94-007-6742-3,
(cid:2)SpringerScience+BusinessMediaDordrecht2014
272 AppendixA:InvestigationofImageQualityofDirac,H.264andH.265
Fig.A.1 EncoderblockdiagramofH.265.Greyboxesareproposedtoolsandwhiteboxesare
H.264/AVCtools[E10](cid:2)2011ETRI
Fig.A.2 DecoderblockdiagramofH.265.Greyboxesareproposedtoolsandwhiteboxesare
H.264/AVCtools[E10](cid:2)2011ETRI
A.3 Image Quality Assessment Using SSIM and FSIM
Digital images and videos are prone to different kinds of distortions during
different phases like acquisition, processing, compression, storage, transmission,
and reproduction [Q2]. This degradation results in poor visual quality. There are
AppendixA:InvestigationofImageQualityofDirac,H.264andH.265 273
several metrics which are widely used to quantify the image quality like FSIM,
SSIM, bitrates, PSNR and MSE [Q28, Q2, Q13, Q17]. The conventional metrics
likePSNRandMSEaredirectlydependentontheintensityofanimageanddonot
correlatewiththesubjectivefidelityratings[Q16]. MSEcannotmodelthehuman
visual system very accurately [Q19]. The measured parameters like PSNR, MSE,
andSSIMofDirac,H.264,andH.265willbecomparedtostudytheircomparative
characteristics and make conclusions.
SSIM is the quality assessment of an image based on the degradation of
structural information [Q13]. The SSIM takes an approach that the human visual
system is adapted to extract structural information from images [Q17]. Thus, it is
importanttoretainthestructuralsignalforimagefidelitymeasurement.Figure A.3
shows the difference between nonstructural and structural distortions. The
nonstructural distortions are changes in parameters like luminance, contrast,
gamma distortion, and spatial shift and are usually caused by environmental and
instrumental conditions occurred during image acquisition and display [Q17]. On
the other hand, structural distortion embraces additive noise, blur, and lossy
compression [Q17]. The structural distortions change the structure of an image
[Q17]. Figure A.4 explains the measurement system used in the calculation of
SSIM.
For given vectors, x¼fxji¼1;2;...;Ng and y¼fyji¼1;2;...;Ng. SSIM
i i
is evaluated on three different metrics like luminance, contrast, and structure
which are described mathematically by Eqs. (A.1), (A.2), and (A.3) respectively
[Q17].
Fig.A.3 Differencebetweennonstructuralandstructuraldistortions[Q22](cid:2)2009IEEE
274 AppendixA:InvestigationofImageQualityofDirac,H.264andH.265
Fig.A.4 BlockdiagramofSSIMmeasurementsystem[Q22](cid:2)2009IEEE
(cid:2) (cid:3) 2lxlyþC1
l x;y ¼ ðA:1Þ
l2þl2þC
x y 1
(cid:2) (cid:3) 2rxryþC2
c x;y ¼ ðA:2Þ
r2þr2þC
x y 2
(cid:2) (cid:3) rxyþC3
s x;y ¼ ðA:3Þ
r r þC
x y 3
Here,
l and l = local sample means of x and y respectively
x y
r and r = local sample standard deviations of x and y respectively
x y
r = local sample correlation coefficient between x and y
xy
C , C , and C = constants that stabilize the computations when denominators
1 2 3
become small
General form of SSIM index can be obtained by combining equations (A.1),
(A.2) and (A.3) [Q27].
(cid:2) (cid:3) h(cid:2) (cid:3)iah (cid:2) (cid:3)ibh (cid:2) (cid:3)ic
SSIM x;y ¼ l x;y c x;y s x;y ðA:4Þ
Here a, b, and c are parameters that mediate the relative importance of those
three components. Using a¼b¼c¼1. We get [Q27],
(cid:2) (cid:3)(cid:2) (cid:3)
(cid:2) (cid:3) 2lxly + C1 2rxyþC2
SSIM x;y ¼(cid:2) (cid:3)(cid:2) (cid:3) ðA:5Þ
l2þl2þC r2r2þC
x y 1 x y 1
AppendixA:InvestigationofImageQualityofDirac,H.264andH.265 275
Fig.A.5 MSEandSSIMmeasurementsofimagesunderdifferentdistortions.aOriginalimage,
(MSE=0,SSIM=1)bmeanluminanceshift,(MSE=144,SSIM=0.988)ccontraststretch,
(MSE = 144, SSIM = 0.913) d impulse noise contamination, (MSE = 144, SSIM = 0.840)
e blurring, (MSE = 144, SSIM = 0.694) and f JPEG [J18] compression (MSE = 142, SSIM =
0.662)[Q13]
Figure A.5 shows the different distorted images which are quantified using
MSE and SSIM. It is clearly visible that the different images are of different
quality based on human visual system (HVS). However, all the distorted images
have approximately same MSE, whereas SSIM is less for poor quality image
giving much better image quality indication than that of MSE.
Afeaturesimilarity(FSIM)indexisbasedonthefactthatHVSunderstandsan
image mainly according to its low-level features [Q28]. The phase congruency
(PC)isadimensionlessmeasureofthesignificanceofalocalstructure[Q28].PC
and image gradient magnitude (GM) measurements are used as primary and
secondary feature respectively in FSIM [Q28]. FSIM score is calculated by
applying PC as a weighting function on the image local quality characterized by
PC and GM [Q28]. FSIM is designed for gray-scale images [Q28] and FSIMc
incorporates the chrominance information. FSIM can be mathematically modeled
as follows [Q28].
P
(cid:2) (cid:3) S ðxÞPC ðxÞ
FSIM x;y ¼ x2X L m ðA:6Þ
P
PC ðxÞ
x2X m
Here, S ðxÞ = overall similarity between reference image and distorted image.
L
FSIMccanbemathematicallymodeledasshownin(A.7)andthecomputation
process is illustrated in Fig. A.6 [Q28].
276 AppendixA:InvestigationofImageQualityofDirac,H.264andH.265
Fig.A.6 IllustrationforFSIM/FSIMcindexcomputation.f isthereferenceimage,andf isa
1 2
distortedversionoff [Q28].(cid:2)2011IEEE
1
P S ðxÞ½S ðxÞ(cid:2)kPC ðxÞ
FSIM ¼ x2X L C m ðA:7Þ
C P PC ðxÞ
x2X m
Here, k[0isthe parameterused toadjusttheimportance ofthe chrominance
components.
P S ðxÞS ðxÞ½SðxÞS ðxÞ(cid:2)kPC ðxÞ
FSIM ¼ x2X PC G I Q m
C P PC ðxÞ
x2X m
All the metrics use different approaches to compare the images quantitatively.
This different approach makes one method different from another. Table A.1
shows the ranking of image quality assessment metric performance on six
databases.ItcanbeseenfromTable A.1thatFSIMisbetterthanSSIMandSSIM
is better than PSNR when implementing an image quality assessment.
TableA.1 Rankingofimagequalityassessmentmetricsperformance(FSIM,SSIMandPSNR)
onsixdatabases[Q28]
TID2008 CSIQ LIVE IVC MICT A57
FSIM 1 1 1 1 1 1
SSIM 2 2 2 2 2 2
PSNR 3 3 3 3 3 3
AppendixA:InvestigationofImageQualityofDirac,H.264andH.265 277
Fig.A.7 OriginalForeman
QCIFsequence[V3].Video
Information.QCIFsequence:
foreman_qcif.yuv.Frame
height:176.Framewidth:
144.Framerate:30frame/s.
Total#offramesusedfor
encoding:30frames
A.4 Results
A.4.1 Results Using Foreman QCIF Sequence
Figures A.9, A.10, A.11 and Table A.2.
A.4.2 Results Using Foreman CIF Sequence
Figures A.14, A.15, A.16 and Table A.3.
A.4.3 Results Using Container QCIF Sequence
Figures A.19, A.20, A.21 and Table A.4.
A.4.4 Results Using Container CIF Sequence
Figures A.24, A.25, A.26, and Table A.5.
A.5 Conclusions
Theappendixisaimed instudyingthequalitativeperformancesofdifferent video
codecs with a primary focus on Dirac, H.264 and H.265 [D24, H36, E50].
Different parameters like PSNR, MSE, and SSIM at various bitrates were
measuredforallthreevideocodecstomakeacomparativestudy.Basedonvarious
test sequences of different spatial/temporal resolutions, MATLAB, Microsoft
VisualStudio,andMSUvideoqualitymeasurementtools(Fig. A.27)[Q30]were
278 AppendixA:InvestigationofImageQualityofDirac,H.264andH.265
Dirac at 87.32 kbps H.264 at 87.6 kbps H.265 at 76.80 kbps
(baseline profile)
Dirac at 152.85 kbps H.264 at 142.82 kbps H.265 at 162.46 kbps
(baseline profile)
Dirac at 397.60 kbps H.264 at 323.76 kbps H.265 at 398.21 kbps
(baseline profile)
Dirac at 4266.92 kbps H.264 at 3667.01 kbps H.265 at 2301.14 kbps
(baseline profile)
Fig.A.8 ForemanQCIFsequenceresultsusingdifferentcodecs[E44]
AppendixA:InvestigationofImageQualityofDirac,H.264andH.265 279
Fig.A.9 PSNRachievedat PSNR vs bitrate
variousbitratesforForeman 60
QCIFsequence[E44]
55
50
B
d 45
n
R i 40 Dirac
N 35
S H.264
P 30
H.265
25
20
0 200 400 600 800 1000
Bitrate (kbps)
Fig.A.10 MSEachievedat MSE vs bitrate
variousbitratesforForeman 50
QCIFsequence[E44] 45
40
35
30
E
S
M 25
Dirac
20
H.264
15
H.265
10
5
0
0 200 400 600 800 1000
Bitrate (kbps)
Fig.A.11 SSIMachievedat SSIM vs bitrate
variousbitratesforForeman 1
QCIFsequence[E44] 0.98
0.96
0.94
ex 0.92
d
n 0.9
M I 0.88 Dirac
SI H.264
S 0.86
H.265
0.84
0.82
0.8
0 200 400 600 800 1000
Bitrate (kbps)
280 AppendixA:InvestigationofImageQualityofDirac,H.264andH.265
TableA.2 TabularresultsforY-componentusingForemanQCIFsequence[E44]
Dirac
Bitrate(kbps) PSNR(dB) MSE SSIM
87.31543 24.2585 243.9108 0.70942
104.1455 27.10295 126.7024 0.80875
152.8516 30.81619 53.88419 0.89656
224.4629 35.58351 17.9775 0.95434
397.6055 39.69524 6.97519 0.97947
805.6953 43.87075 2.66689 0.99047
1615.6 47.5301 1.14834 0.99547
2918.021 50.96503 0.52069 0.99801
4266.925 55.04958 0.20329 0.99934
H.264
Bitrate(kbps) PSNR(dB) MSE SSIM
3667.01 69.941 0.00723 1
3280.58 63.357 0.0309 0.9999
2903.03 58.825 0.08601 0.9996
2352.62 55.135 0.20001 0.9991
1952.06 53.245 0.30894 0.9987
1586.51 51.563 0.45541 0.9981
1205.7 49.597 0.71567 0.9971
954.55 47.961 1.04312 0.9959
736.1 46.25 1.54575 0.9942
540.58 44.37 2.38023 0.9917
418.68 42.799 3.41684 0.9885
323.76 41.293 4.83252 0.9845
240.47 39.632 7.08134 0.9784
182.57 38.186 9.88001 0.972
142.82 36.904 13.27354 0.9652
111.9 35.448 18.55763 0.9544
87.6 34.056 25.56888 0.9426
H.265
Bitrate(kbps) PSNR(dB) MSE SSIM
2301.136 61.0449 0.09232 0.99946
1810.152 56.4716 0.2055 0.99881
1407.104 53.6141 0.34358 0.99812
1080.7 51.5846 0.51131 0.99738
827.016 49.9778 0.71189 0.99655
639.616 48.5619 0.96235 0.99556
502.432 47.2557 1.27745 0.99439
398.208 45.9397 1.70959 0.99277
319.312 44.7036 2.25273 0.99081
255.792 43.3888 3.03526 0.98797
205.376 42.0663 4.09816 0.98408
162.456 40.7085 5.59949 0.97936
(continued)