A hybrid video quality metric for analyzing quality degradation due to frame drop

TL;DR

This paper introduces a hybrid full-reference video quality metric designed to assess perceptual quality degradation caused by spatial, temporal, or combined distortions like frame drops, aligning closely with human visual perception.

Contribution

The paper proposes a novel hybrid metric capable of analyzing various types of video distortions, including frame drops, with improved accuracy over existing methods.

Findings

The metric effectively detects quality degradation due to frame drops.

Results show close alignment with human visual system assessments.

The approach outperforms traditional quality metrics in simulated tests.

Abstract

In last decade, ever growing internet technologies provided platform to share the multimedia data among different communities. As the ultimate users are human subjects who are concerned about quality of visual information, it is often desired to have good resumed perceptual quality of videos, thus arises the need of quality assessment. This paper presents a full reference hybrid video quality metric which is capable to analyse the video quality for spatially or temporally (frame drop) or spatio-temporally distorted video sequences. Simulated results show that the metric efficiently analyses the quality degradation and more closer to the developed human visual system