**7. References**

50 Video Compression

(c)

A qualitative comparison of the objective metrics to the user assessment of interpretability shows strong consistency. Compression of these video products to bitrates below 1,000k bps yields discernable losses in image interpretability. The objective metrics shows a similar knee in the curve. These data suggest that one could estimate loss in interpretability from compression using the objective metrics and derive a prediction of the loss in Video NIIRS. Development of such a model would require conducting a second user experiment to establish the relationship between the subjective interpretability scale used in this study and the published Video NIIRS. The additional data from such an experiment would also

The evaluations and analyses presented in this Chapter characterize the loss in perceived interpretability of motion imagery arising from various compression methods and compression rates. The findings build on previous studies (Irvine *et al.* 2007a; O'Brien *et al.* 2007). The findings are consistent with other evaluations of video compression (Gibson *et al.* 2006; Young *et al.* 2010a). Evaluation of image compression for motion imagery illustrates how interpretability-based methods can be applied to the analysis of the image chain. We present both objective image metrics and analysts' assessments of various compressed products. The results show good agreement between the two approaches. Further research

(a) (b)

Fig. 9. (a) Original frame and compressed using (b) H.264 and (c) MPEG.

support validation of a model for predicting loss due to compression.

**6. Conclusion** 


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**Part 2** 

**Motion Estimation** 

