SCCOME: Scene Change Capture and Optical Motion Estimation for Video Quality Assessment

Tsung-Jung Liu, Hao-Shiang Liao, Kuan-Hsien Liu

Abstract: The rapid growth of user-generated content (UGC) on social platforms has created a pressing need for effective outdoor video quality assessment. Evaluating video quality in uncontrolled environments is challenging due to the absence of reference videos and distortions caused by compression and transmission artifacts, limiting the applicability of traditional metrics. In this paper, we propose a novel no-reference video quality assessment model that reduces computational complexity by identifying frames with significant visual changes. Optical flow detection is then applied to these frames to capture perceptually important regions, enabling focused processing. Experiments on three public outdoor video quality databases—KoNViD-1k, LIVE-Qualcomm, and CVD2014—demonstrate the effectiveness of our method. Furthermore, ablation studies highlight the critical roles of frame selection and optical flow-based region analysis in improving model performance. The source code is available at https://github.com/Hsiang417/SCCOME.