shreyanka multimedia report

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EE 5359 FALL 2010

MULTIMEDIA PROCESSINGPROJECT REPORT

Performance Analysis and Comparison of H.264 Intraframe

coding, JPEG, JPEG-LS, JPEG-2000, JPEG-XR and AVS

China Intraframe

INSTRUCTOR: DR. K. R. RAO

Shreyanka Subbarayappa

Department of Electrical Engineering

University of Texas at Arlington

Email: [email protected]

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mailto:[email protected]:[email protected]


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List of acronymsAIC advanced image coding

AVC advanced video coding

AVS audio video standard

BMP bit map format

CABAC context adaptive binary arithmetic coding

DCT discrete cosine transform

DWT discrete wavelet transform

EBCOT embedded block coding with optimized truncation

EZW embedded zero-tree wavelet coding

FRExt fidelity range extensions

HD-photo high-definition photo

I-frame intra frame

IP intra prediction

JM joint model

JPEG joint photographic experts group

JPEG-LS joint photographic experts group lossless coding

JPEG-XR joint photographic experts group extended range

LBT lapped bi-orthogonal transform

MSE mean square error

PGM portable graymap

PPM portable pixel map

PSNR peak signal to noise ratio

SSIM structural similarity index metric

VLC variable length coding

LIST OF FIGURES

Figure

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1: Different prediction modes used for prediction in AIC and H.264

2: The specific coding parts of the profiles in H.264

3: Basic coding structure for H.264/AVC for a macroblock

4: Block diagram for CABAC

5(a): Zig-zag scan

5(b): Scan line order

6(a): Block diagram of JPEG encoder

6(b): Block diagram of JPEG decoder

7: Structure of JPEG 2000 codec. The structure of the (a) encoder (b) decoder

8: Tiling, DC level shifting, color transformation, DWT of each image component

9: JPEG-LS block diagram

10: Test sequences used

11: Structural similarity (SSIM) measurement system

12: Original and output decoded images

LIST OF TABLES

Table

1: Different parts of AVS

2: Different test sequences used

Performance Analysis and Comparison of H.264 Intraframe

coding, JPEG, JPEG-LS, JPEG-2000, JPEG-XR and AVS

China Intraframe

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Abtract:It is proposed to implement the H.264 intraframe coding using JM software [11]

and compare the results with other image compression techniques like JPEG, JPEG2000,

JPEG-LS, JPEG-XR and AVS China Part7. Coding simulations will be performed on

various sets of test images. Experimental results are to be measured in terms of bit-rate,quality- PSNR, SSIM etc. This project considers only main and (FRExt) high profiles in

H.264/AVC I-frame coding, JPEG using baseline profile, JPEG 2000 in non-scalable, butoptimal mode and AVS China part 7.

Introduction:

H.264 technology aims to provide good video quality at considerably low bit rates,

at reasonable level of complexity while providing flexibility to wide range of applications[2]. Coding efficiency is further improved in fidelity range extensions (FRExt) using 8x8

integer transform and works well for more complex visual content. JPEG [5] is first still

image compression standard which uses 8x8 block based DCT decomposition, while JPEG

2000 is a wavelet-based compression standard which has improved coding performanceover JPEG with additional features like scalability and lossless coding capability has best

performance with smooth spatial data [4]. JPEG performs well in low complexity

applications whereas JPEG 2000 works well in high complexity, lower bit-rateapplications. JPEG2000 has rate-distortion advantage over JPEG. Microsoft HD photo [10]

is a new still-image compression algorithm for continuous-tone photographic images which

maintains highest image quality or delivers the most optimal performance. JPEG-XR [10](extended range), a standard for HD-photo has high dynamic-range image coding and

performance as the most desirable feature. Its performance is close to JPEG2000 with

computational and memory requirement close to JPEG. With half the file size of JPEG, HDphoto delivers lossy compressed image with better perceptual quality than JPEG and

lossless compressed image at 2.5 times smaller than the original image. JPEG-LS [30]

(lossless) is an ISO/ITU-T standard for lossless coding of still images. In addition, it also

provides support for "near-lossless" compression. The main goal of JPEG-LS has been todeliver a low complexity solution for lossless image coding with the best possible

compression efficiency. JPEG uses Huffman coding, H.264/AVC and AIC systems adopts

CABAC encoding technique, and HD photo uses reversible integer-integer-mapping lappedbi-orthogonal transform [7]. LOCO-I (low complexity lossless compression for images), an

algorithm for JPEG-LS uses adaptive prediction, context modeling and Golomb coding [3].

It supports near lossless compression by allowing a fixed maximum sample error.Transcoding converts H.263 compression format to that of H.264 and viceversa.

Although the above mentioned compression techniques are developed for different

signals, they work well for still image compression and hence worthwhile for comparison.

Different softwares like JM software for H.264 [11], JPEG reference software [7] for

JPEG, JasPer [8] for JPEG2000, HD-photo reference software [10], JPEG-LS referencesoftware [9] and AVS Part7 software [28] are used for comparison between different

codecs. The evaluation is carried out using bit rates, different quality assessment metricslike PSNR, SSIM [23] and complexity.

H.264 standard:

H.264 or MPEG-4 part 10 aims at coding video sequences at approximately half the bit rate

compared to MPEG-2 at the same quality. It also aims at having significant improvements

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in coding efficiency using CABAC entropy coder, error robustness and network

friendliness. Parameter set concept, arbitrary slice ordering, flexible macroblock structure,redundant pictures, switched predictive and switched intra pictures have contributed to

error resilience / robustness of this standard. Adaptive (directional) intra prediction (Fig.1)

is one of the factors which contributed to the high coding efficiency of this standard [2].

Different modes used for block prediction are shown in Fig.1.

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Fig.1: Different prediction modes used for prediction in AIC and H.264 [1]

Fig.2: The specific coding parts of the profiles in H.264 [2]

Each profile specifies a subset of entire bitstream of syntax and limits that shall besupported by all decoders conforming to that profile. There are three profiles in the first

version: baseline, main, and extended. Main profile is designed for digital storage media

and television broadcasting. H.264 main profile which is the subset of high profile wasdesigned with compression coding efficiency as its main target. Fidelity range extensions

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Arbitrary slice order

Flexible macroblock order

Redundant slice

Bslice

I slice

P slice

CAVLC

Weighted prediction

CABACData partition

SI slice

SP slice

Extended Profile

Main Profile

Baseline Profile

Adaptive transformblock size

Quantization scaling matrices

High Profiles


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[3] provide a major breakthrough with regard to compression efficiency. The profiles are

shown in Fig.2.

There are four High profiles defined in the fidelity range extensions: High, High 10, High

4:2:2, and High 4:4:4. High profile is to support the 8-bit video with 4:2:0 sampling forapplications using high resolution. High 10 profile is to support the 4:2:0 sampling with up

to 10 bits of representation accuracy per sample. High 4:2:2 profile supports up to 10 bitsper sample. High 4:4:4 profile supports up to 4:4:4 chroma sampling up to 12 bits persample thereby supporting efficient lossless region coding [2]. The basic coding structure

for H.264/AVC for a macroblock is shown in Fig.3.

H.264/AVC FRExt High Profile Intra-Frame Coding:

Main feature in FRExt that improves coding efficiency is the 8x8 integer transform- and all

the coding methods as well as prediction modes associated with adaptive selection between

4x4 and 8x8 integer transforms. Other features include

higher resolution for color representation such as YUV 4:2:2 and YUV 4:4:4

addition of 8x8 block size is a key factor in very high resolution, high bit rates

achieve very high fidelity even for selective lossless representation of video

Fig.3: Basic coding structure for H.264/AVC for a macroblock [2].

Context-based Adaptive Binary Arithmetic Coding (CABAC):

CABAC utilizes the arithmetic coding, also in order to achieve good compression.The CABAC encoding process, shown in Fig.4, consists of three elementary steps.

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Fig.4: Block diagram for CABAC [30]

step 1 : binarization Mapping non binary symbols into binary sequence before given to

arithmetic coder.

step 2 : context modeling It is a probability model for defining one or more elementsbased on previously encoded syntax elements.

step 3 : binary arithmetic coding Encodes elements based on selected probability model.

JPEG:

JPEG is the first ISO/ITU-T standard for continuous tone still images [20]. It allows

lossy and lossless coding of still images. JPEG gives good compression results for lossycompression with the least complexity. There are several modes defined for JPEG

including baseline, progressive and hierarchical. The baseline mode, which supports lossy

compression alone, is most popular. Average compression ratio of 15:1 is achieved usinglossy coding with the help of DCT-block based compression. Lossless coding is made

possible with predictive coding compression techniques which include differential coding,

run length coding and Huffman coding. JPEG employs non-uniform quantization withHVS weighting. Zig-zag scanning is performed on quantized coefficients since it allows

entropy coding to be performed in the order from low frequency to high frequency

components as shown in Fig.5(a) and Fig.5(b) [20].

Fig.5(a): Zig-zag scan [31] Fig.5(b): Scan line order [31]

The process flow of JPEG baseline (lossy) algorithm is shown in the Fig.6.

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(a)

(b)

Fig.6(a): Block diagram of JPEG encoder (b): Block diagram of JPEG decoder [5]

The process flow starts with the color conversion for color images followed by 8x8 block

based DCT (process flow starts here for gray scale images), quantization, zig-zag ordering,

and entropy coding using Huffman tables in the encoding process and vice versa fordecoding process. Different quantization matrices are used for luminance and chrominance

components. Quality factor Q is set using quantization tables and different kinds ofartifacts in varied ranges are observed .

JPEG2000

JPEG 2000 [4] is image compression standard which supports lossy and lossless

compression of gray scale or color images. In addition to the compression capability, JPEG

2000 supports excellent low bit rate performance without sacrificing the performance athigh bit rate, region of interest coding, EBCOT (Embedded Block Coding with Optimized

Truncation) which overcomes the limitations of EZW (embedded zero-tree wavelet

coding) which are random access to specific regions of the image, error resilience. It alsosupports flexible file format and progressive decoding of the image to allow from lossless

to lossy by fidelity and resolution. It is a transform based framework, uses wavelet based

decomposition. Wavelet transform has 3dB improvement over DCT based compression .Lossless compression is the result of transform, entropy coding. Consider non-scalable,

single layer mode since scalability feature leads to adverse effect on rate-distortion

performance. Also we disable tiling mode because it also lowers rate-distortion

performance. Tiling allows the image be partitioned into non-overlapped rectangular tiles

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to be encoded independently. The encoder and decoder of JPEG 2000 is shown in Fig.7.

The tiling, DC level shifting, color transformation, DWT of each image component isshown in Fig.8.

Fig.7: Structure of JPEG 2000 codec. The structure of the (a) encoder (b) decoder [4]

Fig.8: Tiling, DC level shifting, color transformation, DWT of each image component [4]

JPEG XR

JPEG XR [32], a coded file format is designed mainly for storage of continuous-

tone photographic content. It supports wide range of color formats including n-channelencodings using fixed and floating point numerical representations, bit depth varieties

giving a way for wide range of data compression scenarios. The ultimate goal is to support

wide range of color encodings, maintain forward compatibility with existing formats and

keep device implementation simple. It also aims at providing same algorithm for lossless aswell as lossy compression.

HD photo format [32] is a new file format standardized using JPEG-XR. Just like

JPEG-2000, Microsoft HD photo works on advanced features like lossy-losslesscompression, bit-rate scalability, editing, region-of-interest decoding, integer

implementation without division etc. on top of compression capability. HD photo

minimizes objectionable spatial artifacts preserving high frequency detail and outperformsother lossy compression technologies in this regard.

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HD photo is a block-based image coder similar to traditional image-coding

paradigm: color conversion, transform, coefficient scanning, scalar quantization andentropy coding. Main blocks of HD photo include transformation stage and the coefficient-

encoding stage. HD photo employs a reversible integer-to-integer-mapping lapped bi-

orthogonal transform (LBT) as its decorrelation engine. The reversible property of thealgorithm supports both lossy and lossless compression. Thus, it simplifies the overall

implementation of the system. HD photos encoder contains many adaptive elements:adaptive coefficient scanning, flexible quantization, inter-block coefficient prediction,adaptive VLC table switching, etc. JPEG XR supports a number of advanced pixel formats

in order to avoid limitations and complexities of conversions between different unsigned

integer representations. This feature allows flexible approach to numerical encoding of

image data. This results in low- complexity implementations in the encoder and decoder.

JPEG-LS

Hewlett Packard proposed a simpler predictive coder for low complexity [18]. LOCO-I(LOw COmplexity LOssless COmpression for Images) is a lossless compression algorithm

for continuous-tone images which combines the simplicity of Huffman coding with the

compression potential of context models. Lossless image compression schemes oftenconsist of two distinct and independent components: modelingand coding. The modeling

part can be formulated as an inductive inference problem, in which an image is observed

pixel by pixel in some pre-defined order (e.g. raster-scan). The block diagram of JPEG-LS

is shown in Fig.9.

Fig.9: JPEG-LS encoder block diagram [14]

AVS CHINA Part7 Intra Frame [26]:

AVS (Audio-video coding standard) is the digital video codec standard developed by China

on a need to reduce the royalty fees paid by the Chinese people for using other international

video coding standards like MPEG-2, MPEG-4 and MPEG-4 part 10 (H.264) . The threemain characteristics of AVS China are that, it is technically an advanced second generation

source coding standard and is totally controlled and formulated by China. At present AVS

China is being used in IP television wherein TV programs are transmitted over the IP

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protocols and is also being tested for Chinese mobile multimedia broadcasting. AVS China

primarily aims at providing high definition and high quality video services. Since the basicsyntax structure of AVS China is very similar to that of MPEG-2 standard it can be easily

used in the present widely used MPEG-2 systems except for the fact that it has a higher

coding efficiency. This refers that AVS China is compatible with the existing MPEG-2systems and has an architecture model very similar to that of H.264 codec. AVS China has

a coding efficiency similar to that of H.264 except that it has lower computationalcomplexity. AVS China has been divided into various parts and thus dividing the AVSChina architecture into varioussub fields. The different parts of AVS China are as shownin Table1.

Table1: Different parts of AVS [25]

DIFFERENCE BETWEEN THE CODECS:

The main difference between the H.264, JPEG, JPEG2000, JPEG-LS and JPEG-

XR codecs is at the transformation stage. JPEG2000 decorrelates image data via the globaldiscrete wavelet transform (DWT) or the more general decomposition of wavelet packet

while H.264 and HD Photo choose the block-based coding framework with the same 16x16

macro-block size and a core 4x4block transform that is very similar to the discrete cosine

transform (DCT), JPEG use discrete cosine transform (DCT) to de-correlate the image. Themajor difference between H.264s and HD Photos transformation stage is the way the two

coders handle inter-block decorrelation. While H.264 relies heavily on adaptive spatial

prediction of the current block from its neighbors, HD Photo employs an overlap operatorwhich performs preprocessing of pixels along the block boundaries before feeding them

into the core DCT-like 4x4block transform. Equivalently, the combination of the overlap

operator and the core block transform generates a lapped transform. Similar to JPEG2000,the entire transform step of HD Photo is constructed with dyadic-rational lifting steps such

that it maps integers to integers with perfect reversibility, allowing a unifying lossless to

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lossy coding framework. On the contrary, H.264 and AVS China achieve lossless

compression from residue coding. Another obvious difference is at the entropy codingstage where each coder tunes its context-based adaptive model to take advantage of the

specific behavior of its transform coefficients and/or parameters. H.264/AVC employs intra

prediction in spatial domain. AVS China follows the same technique. This avoidspropagating the error due to the motion compensation in inter-coded macro-blocks. On the

other hand, all the previous video coding standards like H.263 and MPEG-4 visual use intraprediction in transform domain. LOCO-I significantly outperforms other one-pass schemes(of comparable complexity (e.g. JPEG-Huffman), and it attains compression ratios similar

or superior to those of higher complexity schemes based on arithmetic coding (e.g. JPEG-

Arithmetic). The complexity of JPEG2000 is relatively high, compared with JPEG and

JPEG-LS.

Evaluation Methodology:

Test Images Used:

Image name Resolution Size (bytes) DescriptionLena.bmp 512x512x24 786,486 Classical test image. Smooth color

changes, borders.

Baboon.bmp 512x512x24 786,486 Color image with lots of details and

contains a great variation of color anda large amount of texture

Barbara.bmp 640x512x24 983,094 Color image with many stripes andbasic human face and skin

Lighthouse.bmp 512x640x24 983,094 Color image with many details likefence, natural sky and hand-rail on

top of the lighthouse.

Vegetables.bmp 512x512x24 768,050 Color image with sudden change incolor.

Table2: Different test sequences used

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Fig.10: Test sequences used

Codec Settings:

In the coding experiments, publicly available software implementations are used for

H.264/AVC, JPEG2000, HD photo, JPEG-LS and AVS China. The latest release of the

reference software (JM 17.2) [11] is used for H.264/AVC encoder, and each frame of thetest sequences is coded in the Iframe mode. For JPEG, JPEG baseline reference software

[7] is used. In JPEG, it is used to code each frame to reach the target quality factor to

indirectly control bit rate for lossy coding. For JPEG 2000 coding, M.D. Adams JasPer

(version 1.900.1) software [8] is used. This software is written in C programming language.This software can handle image data in many formats like PGM/PPM, windows BMP, but

it does not accept all the BMP files. In JPEG 2000, it is used to code each frame to reach

target rate specification in terms of compression factors, which is well defined for multi-component images. Table1 shows the different test sequences used with their resolutions,

size in bytes and the description on the image. Fig.10 shows the different test images used.

H.264 INTRAFRAME:

The configuration of the H.264/AVC JM17.2 encoder [11] is chosen as follows:

H.264/AVC JM17.2 configuration for YUV 4:2:0 one frame video formats:

ProfileIDC = 100 # Profile IDC (77=main, FRExt Profiles: 100=High)

LevelIDC = 40 # Level IDC (e.g. 20 = level 2.0)

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IntraProfile = 1 # Activate Intra Profile for FRExt (0: false, 1: true)

Deblocking filter = OFF

YUVFormat = 1 # YUV format (0=4:0:0, 1=4:2:0, 2=4:2:2, 3=4:4:4)

QPISlice = 28 # Quant. param for I Slices, varied from 0 to 51

H.264/AVC JM17.2 configuration for YUV 4:2:2 one frame video formats:

ProfileIDC = 122 # Profile IDC (77=main, FREXT Profiles: 122=High 4:2:2)

LevelIDC = 40 # Level IDC (e.g. 20 = level 2.0)

IntraProfile = 1 # Activate Intra Profile for FRExt (0: false, 1: true)

Deblocking filter=OFF

YUVFormat = 2 # YUV format (0=4:0:0, 1=4:2:0, 2=4:2:2, 3=4:4:4)

QPISlice = 28 # Varied from 0 to 51

JPEG- XR:

For Microsoft HD Photo [10], all options are set to their default values with the only

control coming from the quality factor setting:

No tiling One-level of overlap in the transformation stage

No color space sub-sampling

Spatial bit-stream order

All sub-bands are included without any skipping.

JPEG2000:

For JPEG2000, M.D. Adams JasPer (version 1.900.1) software [8] is used with defaultconfiguration. Quantization value q (0.01 to 1.0) is varied to get different compression

ratios and quality levels.

Encoder:

jasper --input inputfilename.bmp --output outputfilename.jp2 output-format jp2

O rate=0.01

JPEG-LS:

HP LOCO-I [9] software is used for JPEG-LS encoding and decoding with all defaultparameters. Error value e is varied from 1-60 (low quality) to get different compressed

images.

Images should be in ppm or pgm format. Line interleaved mode is considered in the project.

Error value is varied from 1 to 60. Error value of zero corresponds to no

compression.

T1, T2, T3 are thresholds. While giving the settings the following condition needs

to be met. Error value+1


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JPEG-XR:

The JPEG-XR results were obtained by the HD Photo Device Porting Kit version 1.0

available at [10]. The default parameters settings of the HD Photo encoder/decoder

modules were used to encode each original RGB image with 24 bits per pixels. The qualityparameter (q) was varied between 2 and 255 (lossy coding).

wmpdecapp i input.wdp o output.bmp c 0 where c denotes format, c 0 for 24bppBGR, c-2 for 8bppGray

JPEG:

JPEG baseline reference software [7] is used.

Encoder:

cjpeg quality N inputfile.bmp outputfile.jpg

where quality factor N denotes the scale quantization tables to adjust image

quality. Quality factor varies from 1 (worst) to 99 (best); default is 50.

Decoder:

djpeg outfile outputfilename.bmp outputfileformat inputfile.jpg

SUBJECTIVE VS. OBJECTIVE IMAGE QUALITY MEASURES:

Lossless and lossy compression use different methods to evaluate compression quality.Standard criteria like compression ratio, execution time, etc are used to evaluate the

compression in lossless case, which is a simple task whereas in lossy compression, it iscomplex in the sense, it should evaluate both the type and amount of degradation induced inthe reconstructed image [24] .The goal of image quality assessment is to accurately measure

the difference between the original and reconstructed images, the result thus obtained is used

to design optimal image codecs. The objective quality measure like PSNR, measures the

difference between the individual image pixels of original and reconstructed images. TheSSIM [24] is designed to improve on traditional metrics like PSNR and MSE (which have

proved to be inconsistent with human visual perception) and is highly adapted for extracting

structural information. The SSIM index is a full reference metric, in other words, the measureevent of image quality is based on an initial uncompressed or distortion free image as

reference. The SSIM measurement system is shown in Fig. 11.

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Fig.11: Structural similarity (SSIM) measurement system [25]

,2

),(1

22

1

C

Cl

yx

yx

++

+

=

yx

,2

),(2

22

2

C

Cc

yx

yx

++

+

=

yx

,),(3

3

C

Cs

yx

xy

+

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=

yx

where x and y correspond to two different signals that we would like to match, i.e.

two different blocks in two separate images, x ,2

x , and xy the mean of x , the variance

of x , and the covariance of x and y respectively, while C1, C2, and C3 are constants given

by ( )2

11 LKC = , ( )2

22 LKC = , and 2/23 CC = . L is the dynamic range for the sampledata, i.e.L=255 for 8 bit content andK1


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In AIC, color bleeding can be neglected as it does not employ sub-sampling. All

other codecs have this artifact.

Standard distortion metrics can be used for transcoding, rate-distortion control and quality

requirement for new standards.

PERFORMANCE RESULTS AND ANALYSIS:In this section, the results of various still image coding techniques are plotted and

analyzed. For JPEG2000, JPEG-LS, JPEG-XR, JPEG, H.264/AVC Intra frame and AIC,

quality (PSNR) versus bit per pixel and SSIM versus bit per pixel is plotted for all range of

compression. Bits per pixel for all original images is 24bpp. The original and output

decoded images are shown in Fig.12.

Fig.12: Original and output decoded images

LENA: Graph 1: PSNR Vs Bits per pixel plot for Lena Image

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Graph 2: SSIM Vs Bits per pixel plot for Lena Image

BARBARA: Graph 3: PSNR Vs Bits per pixel plot for Barbara Image

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Graph 4: SSIM Vs Bits per pixel plot for Barbara Image

BABOON: Graph 5: PSNR Vs Bits per pixel plot for Baboon Image

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Graph 6: SSIM Vs Bits per pixel plot for Baboon Image

PEPPERS: Graph 7: PSNR Vs Bits per pixel plot for Peppers Image

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Graph 8: SSIM Vs Bits per pixel plot for Peppers Image

LIGHT HOUSE: Graph 9: PSNR Vs Bits per pixel plot for Light House Image

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Graph 10: SSIM Vs Bits per pixel plot for Light House Image

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CONCLUSIONS:

In this project, compression performance was analyzed for H.264/AVC Intra frame coding,

JPEG2000, JPEG-XR (HDPhoto), JPEG-LS, JPEG and AVS China part7 Intraframe. Both

objective and subjective methodologies for the comparison of various still image codingtechniques were used. While PSNR gives an objective quality metrics of image, SSIM

gives subjective quality which takes human visual perception (HVS) into account. Formthe results, the conclusion that can be drawn is that the performance of H.264/AVC Intraframe for 4:2:0 image is better compared to all other images. Test results for different set of

images yield slightly different results for most of the image coding techniques. AVS China

performs better than JPEG, but less compared to JPEG2000 and JPEG-XR. Among all

these JPEG-LS performs poorly at high error rate (low bpp). The performance of thesecoding techniques vary for different sets of images as seen in the graphs (Graph 1- Graph

10).

REFERENCES:

[1] AIC website: http://www.bilsen.com/aic/[2] T. Wiegand, G. Sullivan, G. Bjontegaard and A. Luthra, Overview of the

H.264/AVC Video Coding Standard, IEEE Transactions on Circuits and Systems

for Video Technology, vol. 13, pp.560-576, July 2003[3] K. Sayood, Introduction to Data Compression, Third Edition, Morgan

Kaufmann, 2006.

[4] P. Topiwala, T. Tran and W.Dai, Performance comparison of JPEG2000 and

H.264/AVC high profile intra-frame coding on HD video sequences,Proc. SPIEIntl Symposium, Digital Image Processing, San Diego, vol.6312, pp. 67- 72,Aug.

2006.

[5] G. K. Wallace, The JPEG still picture compression standard, Communication of

the ACM, vol. 34, pp. 31-44, April 1991.[6] I. Richardson, The H.264 advanced video compression standard, 2nd edition,

Wiley, 2010.[7] JPEG reference software website:

ftp://ftp.simtel.net/pub/simtelnet/msdos/graphics/jpegsr6.zip

[8] JPEG2000 latest reference software (Jasper Version 1.900.0)

[9] Website: http://www.ece.uvic.ca/~mdadams/jasper/[10] JPEG-LS reference software website http://www.hpl.hp.com/loco/

[11] Microsoft HD photo specification:

http://www.microsoft.com/whdc/xps/wmphotoeula.mspx[12] H.264/AVC reference software (JM 17.2) Website:

http://iphome.hhi.de/suehring/tml/download/[13] JPEG Encoder and Decoder Block Diagrams :

http://www.cmlab.csie.ntu.edu.tw/cml/dsp/training/coding/jpeg/jpeg/decoder.htm

[14] JPEG-LS Block Diagram: http://pam.wikipedia.org/wiki/Lossless_JPEG

[15] H.264 Encoder Block Diagram :http://conf.ncku.edu.tw/research/articles/e/20071102/2.html

[16] H.264 Decoder Block Diagram :

http://www.allgosystems.com/html/h264_arm11.htm

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[17] A.Skodras, et al, The JPEG2000 still image compression standard, IEEE

Signal Processing Mgazine, vol.17, pp.1-144, Jan. 2002.[18] M. J. Weinberger, G. Seroussi and G. Sapiro, The LOCO-I lossless image

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NJ: Wiley, 2009.[22] P. Topiwala, Comparative study of JPEG2000 and H.264/AVC FRExt I-frame

coding on high definition video sequences,Proc. SPIE Intl Symposium,

Digital Image Processing, San Diego, vol. 5909, issue.1, pp. 763-771, Aug.2005.

[23] T. Wiegand et al, Overview of the H.264/AVC Video Coding Standard, IEEE

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[24] Z. Wang, et al, Image quality assessment: from error visibility to structural

similarity, IEEE Trans. on image processing, vol. 13, pp. 600 612, Apr. 2004.[25] W.B.Pennebaker and J.L.Mitchell, The JPEG data compression standard for

continuous tone still images, Van Nostrand, 1992.

[26] Special Issue on AVS and its Applications in Signal Processing : Image

Communication , vol.24, 2009.[27] X. Ji,et al, B-picture coding in AVS video compression standard, Signal

Processing: Image Communication, vol.23, pp.31-41,Jan. 2008 .

[28] AVS China software can be downloaded from the following site

ftp://159.226.42.57/public/avs_doc/avs_software[29] D. Marpe, T.Weigand and G. Sullivan, The H.264/MPEG4 advanced video

coding standards and its applications,IEEE Communications Magazine,vol.44, pp.134-143, Aug. 2006.

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[32] T. Tran, L.Liu and P. Topiwala, Performance comparison of leading imagecodecs: H.264/AVC intra, JPEG 2000, and Microsoft HD photo,Proc. SPIEIntl Symposium, Digital Image Processing, San Diego, Sept. 2007.

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coding, Boca Raton, FL: Taylor and Francis, 2006.

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