Video Quality Assessment System Using Deep Optical Flow and Fourier Property

Abstract

Ensuring superior video quality is essential in various fields such as VFX film production, digital signage, media facades, product advertising, and interactive media, as it directly elevates the viewer's engagement and experience. The ability to accurately quantify a video's visual quality not only influences its valuation but is pivotal in maintaining high standards. Among the attributes influencing video quality, subjective quality stands out, however, several other elements also contribute significantly. Although automated video evaluations offer efficiency, there are situations necessitating expert editorial insight to measure nuanced subjective attributes. Our research primarily focuses on two prevalent issues undermining video quality: erratic camera motions and suboptimal focus. We employed a deep learning-driven optical flow technique to quantify inconsistent camera movements and adopted a Fast Fourier Transform (FFT)-based algorithm for blur detection. Moreover, our proposed adaptive threshold, grounded in statistical analysis, effectively delineates scenes as either desirable or substandard. Testing this framework on a diverse set of videos, we found it proficiently assessed video quality within a practical threshold range.