High Frame Rate Video Quality Assessment using VMAF and Entropic Differences

TL;DR

This paper introduces a fusion of VMAF and GREED features to improve video quality assessment across different frame rates, outperforming individual models on HFR and standard datasets.

Contribution

The paper proposes a novel fusion approach combining VMAF and GREED features to enhance frame rate dependent video quality assessment.

Findings

Fusion improves prediction accuracy for HFR videos.

Fused features outperform individual models on multiple datasets.

Method captures complementary perceptual quality information.

Abstract

The popularity of streaming videos with live, high-action content has led to an increased interest in High Frame Rate (HFR) videos. In this work we address the problem of frame rate dependent Video Quality Assessment (VQA) when the videos to be compared have different frame rate and compression factor. The current VQA models such as VMAF have superior correlation with perceptual judgments when videos to be compared have same frame rates and contain conventional distortions such as compression, scaling etc. However this framework requires additional pre-processing step when videos with different frame rates need to be compared, which can potentially limit its overall performance. Recently, Generalized Entropic Difference (GREED) VQA model was proposed to account for artifacts that arise due to changes in frame rate, and showed superior performance on the LIVE-YT-HFR database which contains frame rate dependent artifacts such as judder, strobing etc. In this paper we propose a simple extension, where the features from VMAF and GREED are fused in order to exploit the advantages of both models. We show through various experiments that the proposed fusion framework results in more efficient features for predicting frame rate dependent video quality. We also evaluate the fused feature set on standard non-HFR VQA databases and obtain superior performance than both GREED and VMAF, indicating the combined feature set captures complimentary perceptual quality information.