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The efficient transmission and storage of digital imagery increasingly requires compression to maintain effective channel bandwidth and device capacity. Unfortunately, in applications where high compression ratios are required, lossy compression transforms tend to produce a wide variety of artifacts in decompressed images. Image quality measures (IQMs) have been published that detect global changes in image configuration resulting from the compression or decompression process. Examples include statistical and correlation-based procedures related to mean-squared error, diffusion of energy from features of interest, and spectral analysis. Additional but sparsely-reported research involves local IQMs that quantify feature distortion in terms of objective or subjective models. In this paper, a suite of spatial exemplars and evaluation procedures is introduced that can elicit and measure a wide range of spatial, statistical, or spectral distortions from an image compression transform T. By applying the test suite to the input of T, performance deficits can be highlighted in the transform's design phase, versus discovery under adverse conditions in field practice. In this study, performance analysis is concerned primarily with the effect of compression artifacts on automated target recognition (ATR) algorithm performance. For example, featural distortion can be measured using linear, curvilinear, polygonal, or elliptical features interspersed with various textures or noise-perturbed backgrounds or objects. These simulated target blobs may themselves be perturbed with various types or levels of noise, thereby facilitating measurement of statistical target-background interactions. By varying target-background contrast, resolution, noise level, and target shape, compression transforms can be stressed to isolate performance deficits. Similar techniques can be employed to test spectral, phase and boundary distortions due to decompression. Applicative examples are taken from ATR practice, with supporting performance analysis of space, time, and computational error associated with measures included in the test suite.
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