Investigation of Image Quality of Dirac, H.264 and H.265
Biju Shrestha (UTA ID: 1000113697 Email: biju.shrestha@mavs.uta.edu)
The University of Texas at Arlington
416 Yates Street, Arlington, Texas 76019-0016
Acronyms and Abbreviations
AVC
advanced video coding
BBC
British Broadcasting Corporation
CBR
constant bit rate
CODEC
coder and decoder
CSNR
channel signal to noise ratio
dB
decibel
FRExt
fidelity range extensions
FSIM
featured similarity index
GM
gradient magnitude
HEVC
high efficiency video coding
HVS
human visual system
IEC
international electrotechnical commission
ISO
international organization for standardization
IST
integer sine transform
ITU-T
international telecommunication union - telecommunication standardization sector
JPEG
joint photographic experts group
kbps
kilobits per second
LIVE
laboratory for image and video engineering
Interim Report for EE 5359: Multimedia Processing
MICT
media information and communication technology laboratory
MPEG
moving picture experts group
MSE
mean squared error
MS SSIM
multi scale structural similarity metric
MSU
Moscow State University
PC
phase congruency
PSNR
peak signal to noise ratio
RGB
red, green and blue
SSIM
structural similarity metric
TID2008
Tampere image database 2008
VBR
variable bit rate
VCEG
video coding experts group
Abstract
There exist several standards for video compression with additional improvements in
performance and qualities in comparison to their older versions [2]. This project proposes to
investigate the image quality of Dirac, H.264 and H.265 using metrics like PSNR, CSNR, MSE,
SSIM, MS SSIM, and FSIM [3, 5, and 7] using various test sequences. The conventional metrics
like PSNR and MSE are a measure of intensity and cannot measure the subjective fidelity [3].
This project report shows the progress made so far.
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Introduction
Video codec is a tool which is used to compress and decompress the digital video [2]. There are
several types of video compression methods. Few of them that are going to be discussed in this
project are Dirac, H.264 and H.265 [1-3].
Dirac
Dirac video codec was initially developed by BBC Research [1]. It is an open source software
project and is powerful and flexible despite using only small number of core tools [1]. The
several features that Dirac offers are [1]:

Multi-resolution transforms

Inter and intra frame coding

Frame and field coding

Dual syntax

CBR and VBR operations

Variable bit depths.

Multiple chroma sampling formats

Lossless and lossy coding

Choice of wavelet filters

Simple stream navigation
Dirac has three main strands [15]. First is a compression specification for the byte stream and the
decoder [15]. Second is software for compression and decompression and third are the
algorithms designed to support simple and efficient hardware implementations [15]. Dirac
despite being similar to many video coding systems had additionally adopted the combined
Interim Report for EE 5359: Multimedia Processing
effectiveness, efficiency and simplicity. The decoder and encoder architectures of Dirac are
shown respectively in figures 1 and 2.
Figure 1. Dirac decoder architecture [18]
Figure 2. Dirac encoder architecture [15]
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H.264
H.264 is also referred as AVC and it is a standard for video compression [2]. H.264/MPEG-4
AVC is one of the international video coding standards jointly developed by the VCEG of the
ITU-T and the MPEG of ISO/IEC [11]. It provides enhanced coding efficiency for a wide range
of applications like video telephony, video conferencing, TV, storage, streaming video, digital
video authoring, digital cinema, etc. [11]. In addition, the FRExt provides enhanced capabilities
relative to the base specification [11].
H.264 does not have a predefined CODEC but has the predefined syntax for decoding and
encoding bit stream as shown in figures 3 and 4 respectively [1]. The various profiles of H.264
are shown in figure 5.
Figure 3. H.264 decoder [2]
Figure 4. H.264 encoder [2]
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Figure 5. Various profile of H.264 [12]
H.265
H.265 is also known as HEVC [3] and it can deliver significantly improved compression
performance relative to that of the AVC (ITU-T H.264 | ISO/IEC 14496-10) [10]. Alshina et al
[16] investigated the coding efficiency with high resolution, HD 1080p, and concluded that it can
be progressed by average 37% and 36% bit savings for hierarchical B structure and IPPP
structure when compared to MPEG-4 AVC [16]. The typical block-based video codec is
composed of many processes including intra prediction and inter prediction, transforms,
quantization, entropy coding, and filtering [17] as shown in Figure 6. Over the decade, video
coding techniques have gone through intensive research to achieve higher coding efficiencies
[17].
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Figure 6. Encoder block diagram of H.265. Grey boxes are proposed tools and white boxes are
H.264/AVC tools [17]
Figure 7. Decoder block diagram of H.265. Grey boxes are proposed tools and white boxes are
H.264/AVC tools [27]
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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 [5]. This
degradation results in poor visual quality. There are several metrics which are widely used to
quantify the image quality like FSIM, SSIM, bitrates, PSNR and MSE [3, 8, 13, 14]. This project
will primarily focus on metrics like SSIM, FSIM and bitrates. The other conventional metrics
like PSNR and MSE will not be measured as they are directly dependent on the intensity of an
image and do not correlate with the subjective fidelity ratings [3]. MSE cannot model the human
visual system very accurately [4].The measured parameters like FSIM and SSIM of Dirac,
H.264, and H.265 will be compared to study their comparative characteristics and make
conclusions.
SSIM is the quality assessment of an image based on the degradation of structural information
[5]. The SSIM takes an approach that the human visual system is adapted to extract structural
information from images [14]. Thus, it is important to retain the structural signal for image
fidelity measurement. Figure 8 shows the difference between nonstructural and structural
distortions. The nonstructural distortions are changes in parameter like luminance, contrast,
gamma distortion, and spatial shift and are usually caused by environmental and instrumental
conditions occurred during image acquisition and display [14]. On the other hand, structural
distortion embraces additive noise, blur, and lossy compression [14]. The structural distortions
change the structure of an image [14]. Figure 9 explains the measurement system used in the
calculation of SSIM.
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Figure 8. Difference between nonstructural and structural distortions [14]
Figure 9. Block diagram of SSIM measurement system [5]
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SSIM is based on the evaluation of three different metrics like luminance, contrast, and structure
which are described mathematically by equations (1), (2), and (3) respectively [7].
--------------------------------------------- (1)
--------------------------------------------- (2)
--------------------------------------------- (3)
Here,
µx and µy = local sample means of x and y respectively
σx and σy = local sample standard deviations of x and y respectively
σxy = local sample correlation coefficient between x and y
C1, C2, and C3 = constants that stabilize the computations when denominators become small
General form of SSIM index can be obtained by combining equations (1), (2) and (3) [7].
------------------------ (4)
Here, α, β, and γ are parameters that mediate the relative importance of those three
components. Using α = β = γ = 1. We get [7],
------------------------ (5)
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Figure 10 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.
(a) Original
MSE = 0; SSIM = 1
(b) Mean luminance shift
MSE = 144, SSIM = 0.988
(c) Contrast stretch
MSE = 144, SSIM = 0.913
(d) Impulse noise
contamination
MSE = 144, SSIM = 0.840
(e) Blurring
MSE = 144, SSIM = 0.694
(f) JPEG compression
MSE = 142, SSIM = 0.662
Figure 10. MSE and SSIM measurement of images under different distortions. (a) original
image, (b) mean luminance shift, (c) contrast stretch, (d) impulse noise contamination, (e)
blurring, and (f) JPEG [22] compression [13]
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FSIM is based on the fact that HVS understands an image mainly according to its low-level
features [3]. PC is a dimensionless measure of the significance of a local structure [3]. PC and
image GM measurements are used as primary and secondary feature respectively in FSIM [3].
FSIM score is calculated by applying PC as a weighting function on the image local quality
characterized by PC and GM [3]. FSIM is designed for gray-scale images [3] and FSIMc
incorporates the chrominance information. FSIM can be mathematically modeled as shown in
equation 6 [3].
---------------------- (6)
Here, SL(x) = overall similarity between reference image and distorted image
FSIMc can be mathematically modeled as shown in equation 7 and the computation process is
illustrated in figure 11 [3].
---------------------- (7)
Here, λ > 0 is the parameter used to adjust the importance of the chrominance components.
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Figure 11. Illustration for FSIM/FSIMc index computation. f1 is the reference image, and f2 is a
distorted version of f1 [3].
All the metrics use different approaches to compare the images quantitavely. This different
approach makes one method different from another. Table 1 shows the ranking of image quality
assessment metric performance on six databases. It can be seen from Table 1 that FSIM is better
than SSIM and SSIM is better than PSNR when implementing an image quality assessment.
Table 1. Ranking of image quality assessment metrics performance (FSIM, SSIM and PSNR) on
six databases [3].
FSIM
SSIM
PSNR
TID2008
1
2
3
CSIQ
1
2
3
LIVE
1
2
3
IVC
1
2
3
MICT
1
2
3
A57
1
2
3
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Results
Video Information
QCIF sequence: foreman_qcif.yuv
Frame height: 176
Frame width: 144
Frame rate: 30 frame/second
Figure 12: “foreman_qcif.yuv” [28]
PSNR vs bitrate
60
PSNR in dB
50
40
30
PSNR (dB) - Y
20
PSNR (dB) - U
PSNR (dB) - V
10
0
0
500
1000
1500
2000
2500
Bitrate (kbps)
Figure 13: PSNR achieved at various bitrates for foreman QCIF sequence using H.264 encoder
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CSNR vs bitrate
60
CSNR in dB
50
40
30
CSNR (dB) - Y
20
CSNR (dB) - U
CSNR (dB) - V
10
0
0
500
1000
1500
2000
2500
Bitrate (kbps)
Figure 14: CSNR achieved at various bitrates for foreman QCIF sequence using H.264 encoder
MSE vs bitrate
35
30
MSE index
25
20
MSE - Y
15
MSE - U
10
MSE - V
5
0
0
500
1000
1500
2000
2500
Bitrate (kpbs)
Figure 15: MSE achieved at various bitrates for foreman QCIF sequence using H.264 encoder
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SSIM index
SSIM vs bitrate
1.01
1
0.99
0.98
0.97
0.96
0.95
0.94
0.93
0.92
SSIM - Y
SSIM - U
SSIM - V
0
500
1000
1500
2000
2500
Bitrate (kbps)
Figure 16: SSIM achieved at various bitrates for foreman QCIF sequence using H.264 encoder
MS SSIM vs bitrate
1.005
1
MS SSIM
0.995
0.99
MS SSIM - Y
0.985
MS SSIM - U
0.98
MS SSIM - V
0.975
0.97
0
500
1000
1500
2000
2500
Bitrate (kbps)
Figure 17: MS SSIM achieved at various bitrates for foreman QCIF sequence using H.264
encoder
Interim Report for EE 5359: Multimedia Processing
PSNR in dB
PSNR vs bitrate
50
45
40
35
30
25
20
15
10
5
0
PSNR (dB) - Y
PSNR (dB) - U
PSNR (dB) - V
0
500
1000
1500
2000
2500
Bitrate (kbps)
Figure 18: PSNR achieved at various bitrates for foreman QCIF sequence using Dirac encoder
MSE vs bitrate
25
MSE index
20
15
MSE - Y
10
MSE - U
5
MSE - V
0
0
500
1000
1500
2000
2500
Bitrate (kpbs)
Figure 19: MSE achieved at various bitrates for foreman QCIF sequence using Dirac encoder
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SSIM vs bitrate
0.98
0.975
SSIM index
0.97
0.965
0.96
0.955
SSIM - Y
0.95
0.945
0.94
0
500
1000
1500
2000
2500
Bitrate (kbps)
Figure 20: SSIM achieved at various bitrates for foreman QCIF sequence using Dirac encoder
MS SSIM vs bitrate
0.997
0.996
MS SSIM
0.995
0.994
0.993
MS SSIM - Y
0.992
0.991
0.99
0
500
1000
1500
2000
2500
Bitrate (kbps)
Figure 21: MS SSIM achieved at various bitrates for foreman QCIF sequence using Dirac
encoder
Interim Report for EE 5359: Multimedia Processing
Conclusions
The project is aimed in studying the qualitative performances of different video codecs with a
primary focus on Dirac, H.264 and H.265 [19 – 21]. Different parameters like PSNR, CSNR,
MSE, SSIM, MS SSIM, and FSIM at various bitrates will be measured for all three video codecs
to make a comparative study. Based on various test sequences of different spatial/temporal
resolutions, MATLAB, Microsoft visual studio, and MSU video quality measurement tools [26]
will be extensively used to perform image quality assessment of different codecs at various bit
rates. Figures 13 to 17 shows the variation of metrics like PSNR, CSNR, MSE, SSIM, and MS
SSIM respectively for various bitrates for foreman QCIF sequence using H.264 encoder. Further
analysis is needed to be done using Dirac and H.265 encoder.
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Interim Report for EE 5359: Multimedia Processing
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