Task-based assessment of image compression methods and products

Benjamin Purman; Matthew Nulle; Bridget Furlong

doi:10.1117/12.2617757

31 May 2022 Task-based assessment of image compression methods and products

Benjamin Purman, Matthew Nulle, Bridget Furlong

Proceedings Volume 12124, Unmanned Systems Technology XXIV; 121240C (2022) https://doi.org/10.1117/12.2617757
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Remotely-operated and autonomous platforms with image and video sensors are being applied to new applications every day, e.g., wildland fire monitoring, search and rescue operations. Further, edge computing devices provide significant onboard computational capability, supporting an increasingly complex range of on-board autonomy and analytics. Combined with real-world wireless network limitations, there is an increasing interest in compressed video and image products. To reduce data volumes, a viable approach is to send a task description or query to the robotic platform. The platform can then publish a highly compressed image product back to an operator that is designed to answer the specific query. We develop a framework for evaluating compressed image products by focusing on their ability to support specific tasks or queries. We develop a task model based on Item-Response Theory and implement it as a multi-level Bayesian model, and we evaluate the utility of this model with an object classification task. We demonstrate the approach by comparing two different image compression methods using inexperienced users recruited with the Amazon Mechanical Turk (AMT) platform. The result is a potential reduction in file size from gigabytes to less than a few megabytes without loss in task performance.

Conference Presentation

Citation Download Citation

Benjamin Purman, Matthew Nulle, and Bridget Furlong "Task-based assessment of image compression methods and products", Proc. SPIE 12124, Unmanned Systems Technology XXIV, 121240C (31 May 2022); https://doi.org/10.1117/12.2617757

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