![](bg4.png)
Table 1: PSNR and MSSIM of reconstructed gray-level test images using DB9 wavelets at different quantized values.
Image Name (Dimension) Number of Quantized Values PSNR (dB) MSSIM
Lenna (512 × 512) 2 37.7780645697 0.9982142147
4 41.8874641847 0.9992823393
6 43.8590907980 0.9994968461
8 44.2931530130 0.9995311420
10 44.3489820362 0.9995364013
Baboon (512 × 512) 2 26.8132232589 0.9595975231
4 31.5997241598 0.9869355763
6 32.9223693072 0.9905018802
8 33.0622299131 0.9908045725
10 33.0762467334 0.9908358964
Pepper (512 × 512) 2 35.1261558539 0.9968488432
4 38.0511064803 0.9982568820
6 40.6144660901 0.9990231546
8 40.9936699886 0.9991423921
10 41.0082447784 0.9991441908
House (512 × 512) 2 31.9886146645 0.9904885967
4 35.7235201624 0.9951268912
6 38.2730683421 0.9975855578
8 38.4299731739 0.9976918850
10 38.4349224964 0.9976960112
the wavelet used for taking DWT and inverse DWT.
As can be seen from fig.3, the images are not dis-
tinguishable visually. Results from table 1 substan-
tiates the claim. For ’Lenna’, minimum and maxi-
mum PSNR observed are 37.77 and 44.38, respec-
tively, with MSSIM being .99 at all quantized num-
bers. Overall, there is increase in PSNR and MSSIM
with the increase of the number of quantized values.
This increase may or may not be effectivefor practical
application. For example, ’Baboon’ will be best rep-
resented with 4 quantized levels because there is sub-
stantial increase of PSNR and MSSIM from quantized
level 2, but compared to quantized 6 to 10, the in-
crease of these higher levels are not effective enough
to be noticed visually.
6 CONCLUSIONS
This paper introduces the concept of non-uniform
quantization for detailed components in the JPEG-
2000 still image standard. We believe that further ex-
ploring this option might lead to the following pro-
gressive results in the current standard: (1) Improved
quality at same image size, (2) Better compression at
same quality, (3) Flexible number of quantized values
based on the actual statistics of wavelet tranformed
image, and (4) Variable step size compared to fixed
step size, maximizing the elimination of redundancy.
However, the results shown in the paper are pre-
liminary and suggestive. It will be interesting to ex-
amine the proposed quantization algorithm when em-
bedded in the JPEG-2000 standard. Factors which
will determine the success of proposed approach
will be actual compressed size, image quality, time
complexity and encoder complexity. Also, there is
wide scope for testing other non-uniform quantiza-
tion schemes, or perhaps creating new quantization
scheme customized to the standard.
REFERENCES
Marcellin, M. W., Gormish, M. J., Bilgin, A. and Boliek,
M. P. (2000). An Overview of JPEG-2000. In Pro-
ceedings of IEEE Data Compression Conference, pp.
523-541.
Marcellin, M. W., Lepley, M. A., Bilgin, A., Flohr, T. J.,
Chinen, T. T., and Kasner, J. H. (2002). An Overview
of quantization in JPEG 2000. In Signal Processing:
Image Communication, vol. 17, pp. 73-84.
Skodras, A., Christopoulos, C., and Ebrahimi, T. (2001).
The JPEG 2000 Still Image Compression Standard. In
IEEE Signal Processing Magazine, pp. 36-58.
Srivastava, M., Yashu, Y., Singh, S. K., and Panigrahi, P. K.
(2011). Multisegmentation through wavelets: Com-
paring the efficacy of Daubechies vs Coiflets. In Con-
ference on Signal Processing and Real Time Operat-
ing System.
Srivastava, M., Singh, S. K., and Panigrahi, P. K. (Unpub-
lished) A Fast Statistical Vanishing Method for Mul-
tilevel Thresholding in Wavelet Domain.
Wang, Z., Bovik, A. C., Sheikh, H. R. and Simoncelli, E.
P. (2004). Image quality assessment: from error visi-
bility to structural similarity. In IEEE Transactions on
Image Processing, pp. 600-612.
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