Video quality classification based home video segmentation

Abstract

Home videos often have some abnormal camera motions, such as camera shaking and irregular camera motions, which cause the degradation of visual quality. To remove bad quality segments and automatic stabilize shaky ones are necessary steps for home video archiving. In this paper, we proposed a novel segmentation algorithm for home video based on video quality classification. According to three important properties of motion, speed, direction, and acceleration, the effects caused by camera motion are classified into four categories: blurred, shaky, inconsistent and stable using support vector machines (SVMs). Based on the classification, a multi-scale sliding window is employed to parse video sequence into different segments along time axis, and each of these segments is labeled as one of camera motion effects. The effectiveness of the proposed approach has been validated by extensive experiments.