Capturing Video Frame Rate Variations via Entropic Differencing
This work addresses the need for accurate video quality assessment to optimize bandwidth and quality trade-offs in entertainment and streaming, though it is incremental as it builds on existing statistical methods for a specific domain.
The paper tackled the problem of measuring video quality differences between reference and distorted videos with varying frame rates by developing a novel entropic differencing method based on a Generalized Gaussian Distribution model. The result was a highly generalizable model that achieved state-of-the-art performance, correlating well with subjective scores on the LIVE-YT-HFR database.
High frame rate videos are increasingly getting popular in recent years, driven by the strong requirements of the entertainment and streaming industries to provide high quality of experiences to consumers. To achieve the best trade-offs between the bandwidth requirements and video quality in terms of frame rate adaptation, it is imperative to understand the effects of frame rate on video quality. In this direction, we devise a novel statistical entropic differencing method based on a Generalized Gaussian Distribution model expressed in the spatial and temporal band-pass domains, which measures the difference in quality between reference and distorted videos. The proposed design is highly generalizable and can be employed when the reference and distorted sequences have different frame rates. Our proposed model correlates very well with subjective scores in the recently proposed LIVE-YT-HFR database and achieves state of the art performance when compared with existing methodologies.