Task-Oriented Video Compression via XAI-Guided Frame Filtering

Task-oriented video compression aims to eliminate redundancy while preserving task-critical information. However, existing spatial domain methods incur high computational overhead, whereas temporal domain approaches often rely on black-box confidence scores that may discard taskrelevant frames. These methods follow an implicit informationpreservation paradigm that entangles redundancy and relevance, yielding opaque decision-making. In this paper, we therefore reformulate it as an explicit and explainable frame filtering method. Instead of relying on black-box confidence scores, task relevance is quantified by an explainable saliency score grounded in explicit model attribution and statistical aggregation, providing a transparent criterion for frame filtering. This offline-defined saliency metric supervises a lightweight online regressor, so that each online decision inherits the same explainable semantic meaning while avoiding heavy computation at the edge. Specifically, frame filtering is decomposed into two sequential and explainable steps: a similarity detector first removes structurally redundant frames, and a saliency-guided filter then discards frames with limited contribution to the downstream task. Experimental results demonstrate the effectiveness of the proposed method.