**2.1 Data compression**

38 Video Compression

problem, including target motion, camera motion, and frame rate, and the nature of the analysis tasks (Hands 2004; Huynh-Thu *et al.* 2011; Moorthy *et al.* 2010). Factors affecting perceived interpretability of motion imagery include the ground sample distance (GSD) of the imagery, motion of the targets, motion of the camera, frame rate (temporal resolution), viewing geometry, and scene complexity. These factors have been explored and

 Spatial resolution: Evaluations shows that for motion imagery the interpretability of an video clip exhibits a linear relationship with the natural log of the ground sample distance (GSD), at least for clips where the GSD is fairly constant over the clip (Cermak

 Motion and Complexity: User perception evaluations assessed the effects of target motion, camera motion, and scene complexity on perceived image quality (Irvine *et al.* 2006b). The evaluations indicated that target motion has a significant positive effect on

perceived image quality, whereas camera motion has a barely discernable effect. Frame Rate: These evaluations assessed object detection and identification and other image exploitation tasks as a function of frame rate and contrast (Fenimore *et al.* 2006). The study demonstrated that an analyst's ability to detect and recognize objects of interest degrades at frame rates below 15 frames per second. Furthermore, the effect of

 Task Performance: The evaluations assessed the ability of imagery analysts to perform various image exploitation tasks with motion imagery. The tasks included detection and recognition of objects, as might be done with still imagery and the detection and recognition of activities, which relies on the dynamic nature of motion imagery (Irvine *et al.* 2006b; Irvine *et al.* 2007c). Analysts exhibited good consistency in the performance of these tasks. In addition, dynamic exploitation tasks that require detection and

Building on these perceptions studies, a new Video NIIRS was developed (Petitti *et al.* 2009; Young *et al.* 2009). The work presented in this paper quantifies video interpretability using a 100-point scale described in Section 3 (Irvine *et al.* 2007a; Irvine *et al.* 2007b; Irvine *et al.* 2007c). The scale development methodologies imply that each scale is a linear transform of the other, although this relationship has not been validated (Irvine *et al.* 2006a; Irvine *et al.* 2006b). Other methods for measuring video image quality frequently focus on objective functions of the imagery data, rather than perception of the potential utility of the imagery to support specific types of analysis (Watson *et al.* 2001; Watson and Kreslake 2001; Winkler

A recent study of compression for motion imagery focused on objective performance of target detection and target tracking tasks to quantify the information loss due to compression (Gibson *et al.* 2006). Gibson *et al.* (2006) leverage recent work aimed at quantification of the interpretability of motion imagery (Irvine *et al.* 2007b). Using techniques developed in these earlier studies, this paper presents a user evaluation of the interpretability of motion imagery compressed under three methods and various bitrates. The interpretability of the native, uncompressed imagery establishes the reference for comparison (He and Xiong 2006;

characterized in a series of evaluations with experienced imagery analysts:

*et al.* 2011; Irvine *et al.* 2004; Irvine *et al.* 2005; Irvine *et al.* 2007b) .

reduced frame rate is more pronounced with low contrast targets.

recognition of activities are sensitive to the frame rate of the video clip.

2001; Winkler *et al.* 2001).

**2. Image compression** 

Hewage *et al.* 2009; Yang *et al.* 2010; Yasakethu *et al.* 2009).

The dataset for the study consisted of the original (uncompressed) motion imagery clips and clips compressed by three compression methods at various compression rates (Abomhara *et al.* 2010). The three compression methods were:


All three were exercised in intraframe mode. Each of the parent clips was compressed to three megabits per second, representing a modest level of compression. In addition, each parent clip was severely compressed to examine the limits of the codecs. Actual bitrates for these severe cases depend on the individual clip and codec. The choice of compression methods and levels supports two goals: comparison across codecs and comparisons of the same compression method at varying bitrates. Table 1 shows the combinations represented in the study. We recorded the actual bit rate for each product and use this as a covariate in the analysis.

The study used the Kakadu implementation of JPEG2000, the Vanguard Software Solutions, Inc. implementation of H.264, and the Adobe Premiere's MPEG-2 codec. In each case, the 300 key frame interval was used for interframe compression unless otherwise noted. Intraframe encoding is comparable to interframe encoding with 1 key frame interval.

The study used progressive scan motion imagery in a 848 x 480 pixel raster at 30 frames per second (f/s). Since most of the desirable source material was available to us in 720 P HD video, a conversion process was employed to generate the lower resolution/lower frame rate imagery. We evaluated the conversion process to assure the goals of the study could be met. The video clips were converted using Adobe Premiere tools.


Note: the severe bitrate represents the limit of the specific codec on a given clip.

Table 1. Codecs and Compression Rates
