Subjective and Objective Quality Assessment of High-Motion Sports Videos at Low-Bitrates

TL;DR

This paper presents a large-scale subjective and objective quality assessment of low-bitrate sports videos, providing insights into optimal bitrate ladder design for adaptive streaming.

Contribution

It introduces the first extensive dataset of low-bitrate sports videos, evaluates VQA algorithms, and offers recommendations for bitrate ladder optimization.

Findings

Large-scale dataset with 94 participants and 23,000 scores

Benchmarking of VQA algorithms on low-bitrate sports videos

Recommendations for optimal bitrate ladder design

Abstract

Videos often have to be transmitted and stored at low bitrates due to poor network connectivity during adaptive bitrate streaming. Designing optimal bitrate ladders that would select the perceptually-optimized resolution, frame-rate, and compression level for low-bitrate videos for adaptive streaming across the internet is therefore a task of great interest. Towards that end, we conducted the first large-scale study of medium and low-bitrate videos from live sports for two codecs (Elemental AVC and HEVC) and created the Amazon Prime Video Low-Bitrate Sports (APV LBS) dataset. The study involved 94 participants and 742 videos, with more than 23,000 human opinion scores collected in total. We analyzed the data obtained and we also conducted an extensive evaluation of objective Video Quality Assessment (VQA) algorithms and benchmarked their performance, and make recommendations on bitrate ladder design. We're making the metadata and VQA features available at https://github.com/JoshuaEbenezer/lbmfr-public.