radhika veerla radhika.veerla@uta ee graduate student, ut arlington
DESCRIPTION
Presentation for Ambrado Inc. Richardson, Texas, on June 13, 2008 . - PowerPoint PPT PresentationTRANSCRIPT
Implementation of AIC based on I-frame only coding in H.264 and
comparison with other still frame image coding standards such as
JPEG, JPEG 2000, JPEG-LS, JPEG-XR
Radhika Veerla [email protected] Graduate student,
UT Arlington
MULTIMEDIA PROCESSING LAB ELECTRICAL ENGINEERING DEPARTMENT, THE UNIVERSITY OF TEXAS AT ARLINGTON
Presentation for Ambrado Inc. Richardson, Texas, on June 13, 2008
Advanced Image Coding Block Diagram
(a) Encoder [1] (b) Decoder [1]
Advanced Image Coding It is a still image compression system which is a combination of H.264
and JPEG standards.Features: No sub-sampling- higher quality / compression ratios 9 prediction modes as in H.264 Predicted blocks are predicted from previously decoded blocks Uses DCT to transform 8x8 residual block instead of transform
coefficients as in JPEG Employs uniform quantization Uses floating point algorithm Coefficients transmitted in scan-line order Makes use of CABAC similar to H.264 with several contexts
Proposed AIC Algorithm
(a) Proposed AIC Encoder
(b) Proposed AIC Decoder
BGRCr
CbYCC
Mode Selectand Store
BlockPredict
modeY
Y, Cb, Cr Blks+
+
Pred Blk
FDCT Q ZZ Huff
AAC
Q1IDCT+
Table
Res
Res
Dec
Y Dec
YDec
Cb
Dec
Cr
Predictor
ModeEnc
BG
R
CrCbYICC
BlockPredict
Y,Cb,Cr Blks+
+Pred Blk
IDCT Q1 IZZ IHuff
AADTable
Res
ModeDecand Store
modeDecYDecCbDecCr
CC - color conversion, ICC - Inverse CC, ZZ – zig-zag scan, IZZ – inverse ZZ, AAC – adaptive arithmetic coder, AAD – AA decoder.
H.264 Block Diagram
[2]
H.264 Main Profile Intra-Frame Coding
•Transform block size reduced from 8x8 to 4x4•H.264 relies on spatial prediction taking the advantage of inter-block spatial correlation•Uses multiplier-less integer transforms and implemented in 16-bit fixed point architectures•Block DCT with inter-block correlation is competitive with global wavelet coding used in JPEG2000•Use CABAC or CAVLC
H.264 High Profile Intra-Frame Coding•H.264 Fidelity range extensions support higher-resolution color spaces •Advantage- improves coding efficiency by adding 8x8 integer transform, prediction schemes associated with adaptive selection between 4x4 and 8x8 transforms
JPEG Encoder and Decoder
(a) Encoder [6]
(b) Decoder [6]
JPEG-Baseline•8x8 block based DCT•Scalar quantization •Different quantization tables for luminance and chrominance components•Huffman coding
JPEG2000•Relies on wavelet transform•EBCOT scheme for coding wavelet coefficients•Adaptive context-based binary arithmetic coding•This project disables tiling and scalable mode for comparison as they adversely affect rate-distortion performance
Evaluation MethodologySoftwares and parameters used for
comparison Standards Software Parameter
Setting
AIC AIC reference quality JPEG JPEG-Baseline
Ref.quality
H.264 JM software quantizationJPEG 2000 JasPer rateJPEG-XR JPEG-XR ref. quality
Codec SettingsH.264• Main and high profiles in 4:2:0 coding mode• Activate intra coding profile for Frext• Activate RGB coding mode• 8x8 transform mode: enabled, allowing adaptive choice between (4x4) /(8x8)
transform and all associated prediction modes• Motion estimation: enabled• CABAC: enabled• R-D optimization: enabled • De-blocking filter: enabled
HD photo• 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
Image Quality Measures• Criteria to evaluate compression quality• Two types of quality measures Objective quality measure- PSNR, MSE Structural quality measure- SSIM• MSE and PSNR for a NxM pixel image are defined as
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.
M
m
N
n
nmynmxNM
MSE1 1
2,,*1 (1)
MSELPSNR
2
10log10 (2)
Structural Similarity Method• This method emphasizes that the Human Visual System (HVS) is
highly adapted to extract structural information from visual scenes. Therefore, structural similarity measurement should provide a good approximation to perceptual image quality.
• The SSIM index is defined as a product of luminance, contrast and structural comparison functions. [14]
where μ is the mean intensity, and σ is the standard deviation as a round estimate of the signal contrast. C1 and C2 are constants. M is the numbers of samples in the quality map.
Results for same resolutions
(a) Lena (512x512x24) (b) Airplane (512x512x24)
(c) Peppers (512x512x24) (d) Sailboat (512x512x24)
0 2 4 6 8 10 1215
20
25
30
35
40
45
50
55
60
65
Bits Per Pixel
PS
NR
dB
Quality vs Compression
AICAIC-HuffmanAIC-Adapt-ACJPEG-RefJPEGJPEG2000H.264-MainH.264-High
0 1 2 3 4 5 6 7 8 9 1015
20
25
30
35
40
45
50
55
60
Bits Per Pixel
PS
NR
dB
Quality vs Compression
AICAIC-HuffmanAIC-Adapt-ACJPEG-RefJPEGJPEG2000H.264
0 2 4 6 8 10 12 1410
20
30
40
50
60
70
Bits Per Pixel
PS
NR
dB
Quality vs Compression
AICAIC-HuffmanAIC-Adapt-ACJPEG-RefJPEGJPEG2000H.264
0 5 10 1510
15
20
25
30
35
40
45
50
55
Bits Per Pixel
PS
NR
dB
Quality vs Compression
AICAIC-HuffmanAIC-Adapt-ACJPEG-RefJPEGJPEG2000H.264
Results (contd.)
(e) Splash (512x512x24) (f) Couple (256x256x24)
(g) Cameraman (256x256x8) (h) Man (256x256x8)
0 1 2 3 4 5 6 7 8 9 1010
20
30
40
50
60
70
Bits Per Pixel
PS
NR
dB
Quality vs Compression
AICAIC-HuffmanAIC-Adapt-ACJPEG-RefJPEGJPEG2000H.264
0 2 4 6 8 10 1215
20
25
30
35
40
45
50
55
60
Bits Per Pixel
PS
NR
dB
Quality vs Compression
AICAIC-HuffmanAIC-Adapt-ACJPEG-RefJPEGJPEG2000H.264-Main
0 1 2 3 4 5 610
20
30
40
50
60
70
80
90
Bits Per Pixel
PS
NR
dB
Quality vs Compression
AIC-HuffmanJPEG-RefJPEG2000H.264
0 0.5 1 1.5 2 2.5 3 3.5 4 4.510
20
30
40
50
60
70
80
Bits Per Pixel
PS
NR
dB
Quality vs Compression
AIC-HuffmanJPEG-RefJPEG2000H.264
Results for different resolutions (contd.)
(i) Lena (32x32x24) (j) Lena (64x64x24)
(l) Lena (256x256x24) (k) Lena (128x128x24)
0 5 10 15 20 2515
20
25
30
35
40
45
50
55
60
Bits Per Pixel
PS
NR
dB
Quality vs Compression
AIC-HuffmanJPEG-RefJPEG2000H.264
0 2 4 6 8 10 12 14 16 1815
20
25
30
35
40
45
50
55
60
Bits Per Pixel
PS
NR
dB
Quality vs Compression
AIC-HuffmanJPEG-RefJPEG2000H.264
0 2 4 6 8 10 12 1415
20
25
30
35
40
45
50
55
60
Bits Per Pixel
PS
NR
dB
Quality vs Compression
AIC-HuffmanJPEG-RefJPEG2000H.264
0 2 4 6 8 10 12 1420
30
40
50
60
70
80
Bits Per Pixel
PS
NR
dB
Quality vs Compression
AIC-HuffmanJPEG-RefJPEG2000H.264
Conclusions and Future Work• AIC outperforms JPEG by about 5dB and performs similar to or surpasses
the JPEG2000 performance below 2bpp.• Typical bit rates for AIC are 0-2bpp for color images and 0-4bpp for gray
scale images • H.264 outperforms every other codec for images of all resolutions, but
works close to other codecs in case of gray-scale images. The main concern is its complexity.
• For gray scale images, all the codecs including H.264 have similar performance.
• The gap between various standards increases with decrease in image resolutions.
• The limitation of JPEG reference software is that it has low dynamic range.• AIC is preferred because of its optimal performance with reduced
complexity and increased speed.• Comparison with JPEG-LS and JPEG-XR and also including SSIM
distortion measurement in Rate-Distortion curves (PSNR and SSIM vs bpp) can be the future work.
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] P. Topiwala, “Comparative study of JPEG2000 and H.264/AVC FRExt I-frame coding on high definition video
sequences,” Proc. SPIE Int’l Symposium, Digital Image Processing, San Diego, Aug. 2005.[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. SPIE Int’l Symposium, Digital Image Processing, San Diego, Aug. 2006.
[5] T.Tran, L.Liu and P. Topiwala, “Performance comparison of leading image codecs: H.264/AVC intra, JPEG 2000, and Microsoft HD photo,” Proc. SPIE Int’l Symposium, Digital Image Processing, San Diego, Sept. 2007.
[6] G. K. Wallace, “The JPEG still picture compression standard,” Communication of the ACM, vol. 34, pp. 31-44, April 1991.
[7] H.264/AVC reference software (JM 12.2) Website: http://iphome.hhi.de/suehring/tml/download/[8] JPEG reference software Website: ftp://ftp.simtel.net/pub/simtelnet/msdos/graphics/jpegsr6.zip[9] JPEG2000 latest reference software (Jasper Version 1.900.0) Website: http://www.ece.ubc.ca/mdadams/jasper[10] Microsoft HD photo specification: http://www.microsoft.com/whdc/xps/wmphotoeula.mspx[11] D. Marpe, V. George, and T.Weigand, “Performance comparison of intra-only H.264/AVC HP and JPEG 2000 for
a set of monochrome ISO/IEC test images”, JVT-M014, pp.18-22, Oct. 2004.[12] M.J. Weinberger, G. Seroussi and G. Sapiro, “The LOCO-I lossless image compression algorithm: principles and
standardization into JPEG-LS”, IEEE Trans. Image Processing, vol. 9, pp. 1309-1324, Aug.2000.http://www.hpl.hp.com/loco/
[13] G. J. Sullivan, “ ISO/IEC 29199-2 (JpegDI part 2 JPEG XR image coding – Specification),” ISO/IEC JTC 1/SC 29/WG1 N 4492, Dec 2007
[14] Z. Wang and A. C. Bovik, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Processing, vol. 13, pp. 600 – 612, Apr. 2004.