Task-Based JPEG 2000 Image Compression: An Information-Theoretic Approach

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, i.e., automated image exploitation algorithms. For machine observers, task-based distortion metrics, such as probability of error, have been shown to be more effective for tasks such as object detection and classification. This motivates an approach to a task-based image compression, within the JPEG 2000 framework, which preserves the information that is most relevant for the given task. Our proposed method produces a JPEG 2000 compliant compressed codestream, which can be decoded by any JPEG 2000 compliant decoder. We demonstrate the feasibility and the effectiveness of our task-based image compression approach on a simple object classification and detection problem and quantify its performance relative to a conventional MSE encoder.