# No-Reference Video Quality Assessment Using Space-Time Chips

arXiv: https://arxiv.org/abs/2008.00031 · HTML: https://mlsanity.com/paper/2008.00031/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 9 of 9)

- [RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content](https://mlsanity.com/paper/2101.10955/) (2021)
- [ChipQA: No-Reference Video Quality Prediction via Space-Time Chips](https://mlsanity.com/paper/2109.08726/) (2021)
- [Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment](https://mlsanity.com/paper/2308.00729/) (2023)
- [Capturing Co-existing Distortions in User-Generated Content for No-reference Video Quality Assessment](https://mlsanity.com/paper/2307.16813/) (2023)
- [HDR or SDR? A Subjective and Objective Study of Scaled and Compressed Videos](https://mlsanity.com/paper/2304.13162/) (2023)
- [GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content](https://mlsanity.com/paper/2305.02422/) (2023)
- [Regression or Classification? New Methods to Evaluate No-Reference Picture and Video Quality Models](https://mlsanity.com/paper/2102.00155/) (2021)
- [HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos](https://mlsanity.com/paper/2304.13156/) (2023)
- [Reinforcement of Explainability of ChatGPT Prompts by Embedding Breast Cancer Self-Screening Rules into AI Responses](https://mlsanity.com/paper/2404.14454/) (2024)

## Abstract (arXiv)

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.