# A real-time blind quality-of-experience assessment metric for HTTP adaptive streaming

arXiv: https://arxiv.org/abs/2303.09818 · HTML: https://mlsanity.com/paper/2303.09818/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2023.

## Built on by (most cited first, 5 of 5)

- [AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2306.04717/) (2023)
- [AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2404.03407/) (2024)
- [Q-Refine: A Perceptual Quality Refiner for AI-Generated Image](https://mlsanity.com/paper/2401.01117/) (2024)
- [CMC-Bench: Towards a New Paradigm of Visual Signal Compression](https://mlsanity.com/paper/2406.09356/) (2024)
- [Subjective and Objective Quality-of-Experience Evaluation Study for Live Video Streaming](https://mlsanity.com/paper/2409.17596/) (2024)

## Abstract (arXiv)

In today's Internet, HTTP Adaptive Streaming (HAS) is the mainstream standard for video streaming, which switches the bitrate of the video content based on an Adaptive BitRate (ABR) algorithm. An effective Quality of Experience (QoE) assessment metric can provide crucial feedback to an ABR algorithm. However, predicting such real-time QoE on the client side is challenging. The QoE prediction requires high consistency with the Human Visual System (HVS), low latency, and blind assessment, which are difficult to realize together. To address this challenge, we analyzed various characteristics of HAS systems and propose a non-uniform sampling metric to reduce time complexity. Furthermore, we design an effective QoE metric that integrates resolution and rebuffering time as the Quality of Service (QoS), as well as spatiotemporal output from a deep neural network and specific switching events as content information. These reward and penalty features are regressed into quality scores with a Support Vector Regression (SVR) model. Experimental results show that the accuracy of our metric outperforms the mainstream blind QoE metrics by 0.3, and its computing time is only 60% of the video playback, indicating that the proposed metric is capable of providing real-time guidance to ABR algorithms and improving the overall performance of HAS.