IMPLEMENTATION AND PERFOMANCE
ANALYSIS OF H.264 INTRA FRAME CODING,
JPEG, JPEG-LS, JPEG-2000 AND JPEG-XR
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EE 5359 Multimedia Project
Amee Solanki (1000740226)
[email protected]
Image Compression
•Compression is the process of compacting data, reducing the number of
bits.
•Reduce redundancy of the image or video data in order to be able to
store or transmit data in an efficient form.
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Fig.1 Comparison of original
coronary angiogram (left) with
two compression results.
Middle: JPEG data compression
by factor of CR=12, Right: factor
of CR=24[14].
Two Types of Compression
Lossless compression:
There is no information loss, and the image can be reconstructed exactly the
same as the original
Applications: medical imagery, archiving
Lossy compression:
Information loss is tolerable.
Applications: commercial distribution (DVD) and rate constrained
environment where lossless methods cannot provide enough compression
ratio
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Evolution of Image Compression
Standards
Fig.2 Evolution of compression technology[15]
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Compression standards
Standard
Software
Main Application
Year
JPEG
JPEG-Baseline Ref.
Image
1992-1999
JPEG-LS
JPEG-LS DLL
Image
1999-2000
*DLL-Dynamic linked
library
JPEG-2000
JasPer
Image
2000
JPEG-XR
JPEG-XR Ref.
Image
2009
H.264/AVC Intra
Coding
JM
Video
2003
Table 1: Comparison of image compression standards[13]
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JPEG Standards
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Baseline JPEG Encoder and Decoder
Fig.2 JPEG encoder block diagram [1]
Fig.3 JPEG decoder block diagram [1]
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JPEG 2000 Encoder and Decoder
Fig. 4 (a) Encoder block diagram (b) Decoder block diagram of JPEG 2000 [2]
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JPEG and JPEG-2000
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Standard
Compression
ratio
Main Compression
Technologies
Main Target
Applications
JPEG
Compression
ratio 2-30
-DCT
-Perceptual
quantization
-Zig zag reordering
-Huffman coding
-Arithmetic coding
-Internet imaging
-Digital photography
-Image and video editing
JPEG-2000
Compression
ratio 2-50
-Wavelets EBCOT
-Internet imaging
-Digital photography
-Image and video Editing
-Printing
-Medical imaging
-Mobile applications
-Color fax
-Satellite imaging
Table 2: Comparison of JPEG and JPEG 2000 [13]
JPEG-LS and JPEG-XR
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Standard
Compression ratio
Main Compression Main Target
Technologies
Applications
JPEG-LS
Compression Ratio
2:1
-Context Modeling
-Prediction
-Golomb Codes
-Arithmetic coding
- Lossless and near
lossless coding of
continuous tone still
images
JPEG-XR
Higher compression
ratio than JPEG
Based on HD Photo
of Microsoft
(Windows Media
Photo)
-Storage and
interchange of
continuous tone
photographic content
(lossless and lossy )
Table 3: Comparison of JPEG-LS and JPEG-XR [13]
H.264/AVC(Advanced Video Coding)
Standard
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H.264 Basics
 H.264/AVC compression video coding is based on the traditional
hybrid concept of block-based motion-compensated prediction (MCP)
and transform coding
 In order to improve the compression efficiency of intra-only
compression, the following two coding tools provide major
contributions to the significant bit rate savings:
• Entropy encoding improvement, CAVLC and CABAC
• Spatial intra prediction conducted by using spatially neighboring samples of a
target block which have been previously coded.
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Spatial Intra prediction
[15]
•H.264/AVC uses both spatial and temporal predictions to increase its
coding gain.
•The intra-only compression uses spatial prediction and the prediction
only occurs within a slice
Fig.4 Examples of spatial intra prediction modes for (8X8) blocks [15]
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•Fig. 5 shows a 4x4 block containing 16 pixels
labeled from a through p. A prediction block p is
calculated based on the pixels labeled A-M
obtained from the neighboring blocks.
Fig. 5 A 4X4 block and its neighboring
pixels[16]
•A prediction mode is a way to generate these
16 predictive pixel values using some or all of
the neighboring pixels in nine different
directions as shown in Fig. 6.
•In some cases, not all of the samples A-M are
available within the current slice.
•In order to preserve independent decoding of
slices, only samples within the current slice are
used for prediction.
Fig. 6 Direction of 9 4X4 intra-prediction [16]
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Fig.7 Examples of spatial intra prediction modes for (4X4) blocks[16]
1. Mode 0 is the vertical prediction mode in which pixels a, e, i, and m are predicted by A and so on.
2. Mode 1 is the horizontal prediction mode in which pixels a,b, c, and d are predicted by I and so on.
3. Mode 2 is called DC prediction in which all pixels i.e. (a to p) as shown in fig. 5 are predicted by
(A+B+C+D+I+J+K+L)/8.
4. For modes 3-8, the predicted samples are formed from a weighted average of the prediction samples
A-M.
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H.264 Basic Encoder and Decoder
Fig.8 H.264 Encoder and decoder block diagrams [3]
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Compressed Image Quality Measures
Criteria to evaluate a compressed image are as follows :
Compression ratio
2. Bit-rate (bandwidth)
3. Objective quality measure- PSNR, MSE (quality of compressed
image)
4. Structural quality measure- SSIM
1.
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PSNR and MSE
 Peak signal-to-noise ratio, often abbreviated PSNR, is the ratio
between the maximum possible power of a signal and the power of
corrupting noise that affects the fidelity of its representation
 MSE and PSNR for a NxM pixel image are defined as
(1)
(2)
where x is the original image and y is the reconstructed image. M and
N are the width and height of an image and ‘L’ is the maximum pixel
value in the NxM pixel image
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Structural Similarity Index
 The structural similarity (SSIM) [17] index is a method for
measuring the similarity between two images
 SSIM is designed to improve on traditional methods like peak
signal-to-noise ratio (PSNR) and mean squared error (MSE),
which have proved to be inconsistent with human eye
perception
 SSIM considers image degradation as perceived change in
structural information. Structural information is the idea that
the pixels have strong inter-dependencies especially when they
are spatially close
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SSIM Metric
[17]
where
x and y correspond to two different signals that need to be compared, i.e. two different blocks in
two separate images ;
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Example with SSIM index
Fig. 9 SSIM Index example [4]
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TABLE OF ACRONYMS
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AVC
advanced video coding
BMP
bit map format
CABAC context adaptive binary arithmetic coding
DCT
discrete cosine transform
EBCOT embedded block coding with optimized truncation
FRExt
fidelity range extensions
GIF
graphics interchange format
HD-photo high-definition photo
HVS
human visual system
I-frame intra frame
JM
joint model
JPEG
joint photographic experts group
JPEG-LS joint photographic experts group lossless and lossless coding
JPEG-XR joint photographic experts group extended range
LBT
lapped bi-orthogonal transform
LOCO-I low complexity lossless compression for images
MSE
mean square error
PSNR
peak signal to noise ratio
SSIM
structural similarity index
VLC
variable length coding
References
[1] JPEG Encoder and Decoder block diagram :
http://www.cmlab.csie.ntu.edu.tw/cml/dsp/training/coding/jpeg/jpeg/decoder.htm
[2] JPEG2000 Encoder and Decoder block diagram :
http://eeweb.poly.edu/~yao/EE3414/JPEG.pdf
[3] H.264 Encoder and Decoder block diagram :
http://www.drtonygeorge.com/video_codec.htm
[4] SSIM Index example diagram:
https://ece.uwaterloo.ca/~z70wang/research/ssim/
[5] H.264/AVC reference software (JM 17.2) website:
http://iphome.hhi.de/suehring/tml/download/
[6] JPEG2000 latest reference software (Jasper Version 1.900.0) website:
http://www.ece.uvic.ca/~mdadams/jasper/
[7] JPEG reference software website:
ftp://ftp.simtel.net/pub/simtelnet/msdos/graphics/jpegsr6.zip
[8] JPEG-LS reference software website:
http://www.hpl.hp.com/loco/
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[9] T. Wiegand, G. J. Sullivan, G. Bjontegaard and A. Luthra,” Overview of the H.264 / AVC
video coding standard ” IEEE Trans. on Circuits and Systems for Video Technology,vol. 13,
pp. 560-576, July 2003.
[10] A.Skodras, C. Christopoulos and T. Ebrahimi, “The JPEG 2000 still image compression
standard”, IEEE Trans. on Signal Processing, vol.18, pp. 36 - 58, Aug 2002.
[11] M. J. Weinberger, G. Seroussi and G. Sapiro, “The LOCO-I lossless image compression
algorithm: principles and standardization into JPEG-LS”, IEEE Trans. on Image Processing, vol.9,
pp.1309-1324, Aug. 2000
[12] C. Christopoulos, A. Skodras and T.Ebrahimi, “The JPEG2000 still image coding system: an
overview”, IEEE Trans. on Consumer Electronics, vol.46, pp.1103-1127, Nov. 2000.
[13] T. Ebrahimi and M. Kunt, “ Visual data compression for multimedia applications”, Proc
IEEE, vol.86, pp. 1109-1125, June 1998.
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[14] Image compression test image:
http://www.uni-kiel.de/Kardiologie/dicom/1999/compression1.html.
[15] Evolution of image compression standards :
ftp://ftp.panasonic.com/pub/panasonic/drivers/PBTS/papers/WP_AVC-Intra.pdf
[16] Intra-prediction modes image:
http://www.atc-labs.com/technology/h264_publication_1.pdf
[17] Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, “Image quality
assessment: From error visibility to structural similarity,” IEEE Trans. on Image
Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004.
[18] I. E. Richardson, “The H.264 advanced video compression standard”, II Edition,
Wiley, 2010.
[19] D. S. Taubman and M. W. Marcellin, "JPEG2000 – Image compression
fundamentals, standards, and practice," Kluwer, 2001.
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