In this case, the new time is updated as in equation (1), i.e., nnew=max(J=n+1, c=n+2)=n+3. In the following iteration, one frame is extracted from the five successive frames, say +3. A pseudo code of the for example the fourth, i.e, J=new proposed algorithm is shown in Table I.


Table 1 shows the PSNR by using the original and new approaches described above. As indicated by these data, the interpolated motion


Fig3A Novel Hyper Spectral_decrypted

Figure 3 : Input video frame


Fig4A Novel Hyper Spectral_decrypted

Figure 4 : Output video frame

vectors fine-tuned by using small search range produce negligible image quality degradation.

Table 1 : PSNR Difference

Sequences (400 frames) Average 



Average PSNR (New algorithm) Difference
Foreman 33.65 33.63 -0.02
salesman 37.74 37.76 0.02
Mthr_dotr 33.57 33.54 -0.03


The primary purpose of this study was to explore the applicability of the commonly used error metrics PSNR and MAE to the spectral analysis of compressed hyper spectral images. This was accomplished through the creation of new error utilities based upon actual spectral analysis techniques which would reliably reflect this application, the results of which were compared with PSNR conferees. Biases were tested which resulted in an improvement in the ability to spectrally analyze reconstructed hyper spectral images (as measured by these new utilities), but that caused a corresponding decrease in PSNR.

There are a number of areas in which this research could be continued in order to provide a usable mechanism for compression of hyper spectral images. One such would involve further exploration into the effect of various types of bias introduced in the spectral dimension and modification of the compression algorithm to accommodate a choice of bias, to be encoded with the compressed file. Another should involve additional spectral analysis tools and the compression of radiance data in order to verify the results as discussed here are applicable to a wide range of usage patterns. Additional compression algorithms should be examined to see how a spectral bias might be applied and tested with the error utilities presented here, as well.

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