**2.2 Experimental design**

The study consists of two parts. Both parts used the set of compression products described above. The first part was an evaluation in which trained imagery analysts viewed the compressed products and the original parent clip to assess the effects of compression on interpretability. The second part of the study implemented a set of computational image metrics and examined their behavior with respect to bitrate and codec. The typical duration of each clip is 10 seconds. Ten video clips were used for this study.

Quantifying Interpretability Loss due to Image Compression 41

Identify vehicles by general class (civilian, military tracked, artillery,

Determine motion and direction of large vehicles (e.g. trains, barges, 18-

Distinguish among a person walking, a person running, and a person riding a

Track a civilian-size moving vehicle (e.g., car, truck, SUV)

Distinguish between an orderly assembly and a panicked crowd

Determine whether a person is getting into or out of a vehicle

The data analysis progresses through several stages: verification and quality control, exploratory analysis to uncover interesting relationships, and statistical modeling to validate findings and formally test hypotheses of interest. The initial analysis examined the data for

Next, we calculated an overall interpretability rating from each analyst for each clip. The method for calculating these ratings was as follows: Each of the three criteria used to rate each clip was calibrated (on a 0-100 scale) in terms of interpretability, where this calibration was derived from an earlier evaluation (Irvine *et al.* 2007c). Multiplying the interpretability level by the IA's confidence rating produces a score for each criterion. The final interpretability score (Equation 1) was the maximum of the three scores for a given clip.

Where Ci,j,k is the confidence rating by the jth IA on the kth clip for the ith criterion and Ii,k is the calibrated interpretability level for that criterion. All subsequent analysis presented below is based on this final interpretability score. The remaining analysis is divided into two sections: interframe compression and intraframe compression. Ultimately, we compared the

Interpretability Score(j, k) = max {Ci,j,k Ii,k : i=1,2,3} / 100 (1)

Identify individual rail cars (e.g. gondola, flat, box) and locomotives by type

Detect large buildings (e.g. hospitals, factories)

Detect presence of freight in open-bed trucks

small vehicle (bicycle, moped, or motorcycle)

Detect gatherings of 5 or more people

(e.g. steam, diesel)

construction)

wheelers)

Table 2. Video Analysis Tasks

anomalies or outliers. None were found in this case.

**3.2 Analysis and findings** 

**Static Criteria** 

**Dynamic** 

**Criteria** 
