A Novel No-reference Video Quality Metric for Evaluating Temporal Jerkiness due to Frame Freezing

TL;DR

This paper introduces a new no-reference video quality metric that accurately assesses the impact of frame freezing on perceived video quality using neural networks and specific features, suitable for real-time applications.

Contribution

The paper presents a novel neural network-based no-reference metric specifically designed to evaluate temporal jerkiness caused by frame freezing, with optimized features and structure for high accuracy.

Findings

High correlation with subjective scores (over 0.9 and 0.8) on training and testing datasets.

Effective in real-time systems due to low complexity.

Validated on multiple video databases with consistent performance.

Abstract

In this work, we propose a novel no-reference (NR) video quality metric that evaluates the impact of frame freezing due to either packet loss or late arrival. Our metric uses a trained neural network acting on features that are chosen to capture the impact of frame freezing on the perceived quality. The considered features include the number of freezes, freeze duration statistics, inter-freeze distance statistics, frame difference before and after the freeze, normal frame difference, and the ratio of them. We use the neural network to find the mapping between features and subjective test scores. We optimize the network structure and the feature selection through a cross validation procedure, using training samples extracted from both VQEG and LIVE video databases. The resulting feature set and network structure yields accurate quality prediction for both the training data containing 54 test videos and a separate testing dataset including 14 videos, with Pearson Correlation Coefficients greater than 0.9 and 0.8 for the training set and the testing set, respectively. Our proposed metric has low complexity and could be utilized in a system with realtime processing constraint.