1. Amee Solanki (1000740226) amee.solanki@mavs.uta.edu
IMPLEMENTATION AND PERFOMANCE ANALYSIS OF H.264 INTRA FRAME CODING, JPEG, JPEG-LS, JPEG-2000 AND JPEG-XR
EE 5359 Multimedia Project
Amee Solanki ( )

2. 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.
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].

3. 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

4. Evolution of Image Compression Standards
Fig.2 Evolution of compression technology[15]

5. Compression standards
Software
Main Application
Year
JPEG
JPEG-Baseline Ref.
Image
JPEG-LS
JPEG-LS DLL
*DLL-Dynamic linked library
JPEG-2000
JasPer
2000
JPEG-XR
JPEG-XR Ref.
2009
H.264/AVC Intra Coding JM
Video
2003
Table 1: Comparison of image compression standards[13]

6. JPEG Standards

7. Baseline JPEG Encoder and Decoder
Fig.2 JPEG encoder block diagram [1]
Fig.3 JPEG decoder block diagram [1]

8. JPEG 2000 Encoder and Decoder
Fig. 4 (a) Encoder block diagram (b) Decoder block diagram of JPEG 2000 [2]

9. JPEG and JPEG-2000 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
-Image and video Editing
-Printing
-Medical imaging
-Mobile applications
-Color fax
-Satellite imaging
Table 2: Comparison of JPEG and JPEG 2000 [13]

10. JPEG-LS and JPEG-XR Standard Compression ratio Main Compression
Technologies
Main Target
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]

11. H.264/AVC(Advanced Video Coding) Standard

12. H.264 Basics Entropy encoding improvement, CAVLC and CABAC
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.

13. 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]

14. Fig. 5 shows a 4x4 block containing 16 pixels labeled from a through p
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.
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. 5 A 4X4 block and its neighboring pixels[16]
Fig. 6 Direction of 9 4X4 intra-prediction [16]

15. Fig.7 Examples of spatial intra prediction modes for (4X4) blocks[16]
Mode 0 is the vertical prediction mode in which pixels a, e, i, and m are predicted by A and so on.
Mode 1 is the horizontal prediction mode in which pixels a,b, c, and d are predicted by I and so on.
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.
For modes 3-8, the predicted samples are formed from a weighted average of the prediction samples A-M.

16. H.264 Basic Encoder and Decoder
Fig.8 (a) H.264 encoder block diagrams [3]

17. Fig.8 (b) H.264 decoder block diagrams [3]

18. Compressed Image Quality Measures
Criteria to evaluate a compressed image are as follows :
Compression ratio
Bit-rate (bandwidth)
Objective quality measure- PSNR, MSE (quality of compressed image)
Structural quality measure- SSIM

19. 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

20. 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

21. 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 ;

22. Example with SSIM index
Fig. 9 SSIM Index example [4]

23. Conclusion

26. TABLE OF ACRONYMS 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

27. References
[1] JPEG encoder and decoder block diagram :
[2] JPEG2000 encoder and decoder block diagram :
[3] H.264 encoder and decoder block diagram : S. Kwon, A. Tamhankar and K.R. Rao, “Overview of H.264 / MPEG-4 Part 10”, J. Visual Communication and Image Representation, vol. 17, pp , April 2006.
[4] SSIM index example diagram:
[5] H.264/AVC reference software (JM 17.2) website:
[6] JPEG2000 latest reference software (Jasper Version ) website:
[7] JPEG reference software website: ftp://ftp.simtel.net/pub/simtelnet/msdos/graphics/jpegsr6.zip
[8] JPEG-LS reference software website:

28. [9] T. Wiegand, G. J. Sullivan, G. Bjontegaard and A
[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 , 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 , 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 , 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 , Nov
[13] T. Ebrahimi and M. Kunt, “ Visual data compression for multimedia applications”, Proc IEEE, vol.86, pp , June 1998.

29. [14] Image compression test image:
[15] Evolution of image compression standards :
ftp://ftp.panasonic.com/pub/panasonic/drivers/PBTS/papers/WP_AVC-Intra.pdf
[16] Intra-prediction modes image:
[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 , Apr
[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.

30. Some Important terms
Quality factor –N denotes the scale quantization tables to adjust image quality. Quality factor varies from 0 (worst) to 100 (best); default is 75.
Rate specify target rate as a positive real number. ‘rate’=1 corresponds to no compression. Rate and bits per pixel are related by the expression: compression ratio=24/bpp= 1/rate for a color image and rate=bpp/8 for a gray scale image.
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 need to be met. (Error value+1)<T1<T2<T3.

31. Results