Task-Based Image Compression and X-Ray Threat Detection System Analysis

Abstract

We demonstrate the utility of task-based design and analysis methodology through its application to image compression and X-ray based threat detection system analysis. Two common tasks are considered for image compression: (1) perceptual image quality evaluation in the supra-threshold regime (i.e. with highly visible distortion) and (2) image classification. First, we propose and demonstrate a compression encoding method to optimize the perceptual image quality in supra-threshold regime for human observers. This approach is based on a distortion metric motivated by the HDR-VDP-2 image quality metric that emulates the human visual system’s perception of artifacts that arise in a compressed image. Whereas, most existing perceptual-optimized image compression methods aim to minimize the detectability of compression artifacts in the near-threshold regime, our proposed encoder aims to operate specifically in the supra-threshold regime where the human visual system’s perception of image quality can be quite different. We implement the proposed encoder within the JPEG 2000 image compression standard and demonstrate its advantage over both detectability-based and conventional mean square error (MSE) based encoders. Next, we present an image compression method for a machine/algorithm observer, i.e. an automated image exploitation algorithm. Traditional image compression methods primarily focus on maximizing the fidelity of the compressed image using image quality driven distortion metrics, which are ideally suited for human observers but are not necessarily optimal for machine observers. For machine observers, task-based distortion metrics, such as probability of error, have been shown to be more effective for tasks such as image classification. This motivates an approach to task-based image compression that preserves the relevant task-specific information (TSI). Our TSI-based encoder implementation is also JPEG 2000 compliant and thus inherits all its underlying scalability advantages. We demonstrate the feasibility and the effectiveness of our TSI-based image compression approach for a classical texture classification task and quantify its performance relative to conventional MSE-based encoder. In the last part of this work, we present a task-based information-theoretic framework for a systematic study of checkpoint X-ray systems using transmission (i.e. absorption) measurements. Conventional system performance analysis of threat detection systems confounds the effect of the system architecture choice with the performance of a threat detection algorithm. However, our system analysis approach enables a direct comparison of the fundamental performance limits of disparate hardware architectures, independent of the choice of threat detection algorithm. We compare various transmission measurement designs associated with different system architectures to understand and quantify the effect of spatial and spectral/energy resolution on the fundamental limits of the X-ray system performance for the threat detection task.