No-Reference Video Quality Assessment Using Space-Time Chips

TL;DR

This paper introduces a no-reference video quality assessment method based on analyzing space-time chips, which are localized features capturing motion and spatial information, to predict video quality without needing reference videos.

Contribution

The paper presents ChipQA-0, a novel no-reference VQA model utilizing space-time chips and statistical modeling, demonstrating high correlation with human quality judgments across multiple datasets.

Findings

Achieves high correlation with human quality scores

Effective across various distortion types

Competitive with state-of-the-art VQA models

Abstract

We propose a new prototype model for no-reference video quality assessment (VQA) based on the natural statistics of space-time chips of videos. Space-time chips (ST-chips) are a new, quality-aware feature space which we define as space-time localized cuts of video data in directions that are determined by the local motion flow. We use parametrized distribution fits to the bandpass histograms of space-time chips to characterize quality, and show that the parameters from these models are affected by distortion and can hence be used to objectively predict the quality of videos. Our prototype method, which we call ChipQA-0, is agnostic to the types of distortion affecting the video, and is based on identifying and quantifying deviations from the expected statistics of natural, undistorted ST-chips in order to predict video quality. We train and test our resulting model on several large VQA databases and show that our model achieves high correlation against human judgments of video quality and is competitive with state-of-the-art models.