Capturing Video Frame Rate Variations via Entropic Differencing

TL;DR

This paper introduces a novel entropic differencing method based on a Generalized Gaussian Distribution to assess video quality across varying frame rates, effectively correlating with subjective scores and outperforming existing methods.

Contribution

The paper presents a new statistical approach for measuring video quality differences that works well even when reference and distorted videos have different frame rates.

Findings

High correlation with subjective quality scores.

State-of-the-art performance on LIVE-YT-HFR database.

Effective across different frame rate variations.

Abstract

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.