Transform coding techniques for lossy hyperspectral data compression

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (SUBMITTED DEC. 2005) 23
90
85
80
PSNR (dB)
75
70
65
60
Full−complexity KLT + JPEG 2000
Low−complexity KLT ( ρ= 0.01) + JPEG 2000
DWT1D + JPEG 2000
3D−SPIHT
55
0 0.5 1 1.5 2 2.5
rate ( bpp)
Fig. 13. Performance evaluation of the proposed JPEG 2000 based technique: rate-distortion curve for the Cuprite
scene. Dashed: Full-complexity KLT. Solid: low-complexity KLT, ρ = 0.01. Dotted+star: DWT1D2D as proposed in
[16]. Solid+star: 3D-SPIHT.
and SPECK are compared; note that, in the table, results are given in terms of signal-to-noise ratio
(SNR) rather than PSNR.
Consistently with the results reported above, also on the reflectance data the performance loss of
the low-complexity KLT with respect to the full-complexity one does not exceed 0.5 dB. The lowcomplexity
KLT has a gain of about 7 dB with respect to the scheme in [16] employing the hybrid
rectangular/square DWT, even though for high bit-rate the performance gap decreases. The proposed
scheme exhibits a significant gain also with respect to SPECK; the SNR gain is even more remarkable,
ranging from 5 to more than 10 dB. This gain is mainly due to two factors. The former is the improved
coding efficiency of the KLT with respect to the spectral DWPT employd in SPECK. The latter is
the 3D post-compression rate-distortion optimization, which is more flexible in selecting the portions
of the 3D set of transform coefficients that contribute more significantly to the reconstructed image
quality.
C. Impact on image exploitation
As is well-known, quality metrics such as PSNR, which are based on the MSE, measure the
fidelity of the reconstructed image with respect to the original image. However, higher PSNR may not
necessarily yield higher quality of a remote sensing lossy-compressed image for a given application.
In fact, some artifacts, e.g. tiling, which may have little effect on PSNR, could heavily bias the