GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content

Chen, Yu Chih; Saha, Avinab; Davis, Chase; Qiu, Bo; Wang, Xiaoming; Gowda, Rahul; Kasavounidis, Ioannis; Bovik, Alan C.

doi:10.1109/lsp.2023.3255011

Cited by 6 publications

(1 citation statement)

References 34 publications

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“…More recent NR-VQA Cloud Gaming shows that deep learning-based models can boost performance for Cloud Gaming VQA task. These include NDNet-Gaming (Utke et al, 2020), DEMI (Zadtootaghaj et al, 2020a), and GAMIVAL (Chen et al, 2023). NDNet-Gaming and DEMI employ a complex Densenet-121 (Huang et al, 2016) deep learning backbone.…”

Section: Cloud Gaming Vqa Algorithmsmentioning

confidence: 99%

Perceptual video quality assessment: the journey continues!

Saha,

Pentapati,

Shang

et al. 2023

Front. Signal Process.

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Perceptual Video Quality Assessment (VQA) is one of the most fundamental and challenging problems in the field of Video Engineering. Along with video compression, it has become one of two dominant theoretical and algorithmic technologies in television streaming and social media. Over the last 2 decades, the volume of video traffic over the internet has grown exponentially, powered by rapid advancements in cloud services, faster video compression technologies, and increased access to high-speed, low-latency wireless internet connectivity. This has given rise to issues related to delivering extraordinary volumes of picture and video data to an increasingly sophisticated and demanding global audience. Consequently, developing algorithms to measure the quality of pictures and videos as perceived by humans has become increasingly critical since these algorithms can be used to perceptually optimize trade-offs between quality and bandwidth consumption. VQA models have evolved from algorithms developed for generic 2D videos to specialized algorithms explicitly designed for on-demand video streaming, user-generated content (UGC), virtual and augmented reality (VR and AR), cloud gaming, high dynamic range (HDR), and high frame rate (HFR) scenarios. Along the way, we also describe the advancement in algorithm design, beginning with traditional hand-crafted feature-based methods and finishing with current deep-learning models powering accurate VQA algorithms. We also discuss the evolution of Subjective Video Quality databases containing videos and human-annotated quality scores, which are the necessary tools to create, test, compare, and benchmark VQA algorithms. To finish, we discuss emerging trends in VQA algorithm design and general perspectives on the evolution of Video Quality Assessment in the foreseeable future.

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Section: Cloud Gaming Vqa Algorithmsmentioning

confidence: 99%

Perceptual video quality assessment: the journey continues!

Saha,

Pentapati,

Shang

et al. 2023

Front. Signal Process.

Self Cite

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Cloud‐based video streaming services: Trends, challenges, and opportunities

Kumar,

Sharma,

Tanwar

et al. 2024

CAAI Trans on Intel Tech

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Cloud computing has drastically changed the delivery and consumption of live streaming content. The designs, challenges, and possible uses of cloud computing for live streaming are studied. A comprehensive overview of the technical and business issues surrounding cloud‐based live streaming is provided, including the benefits of cloud computing, the various live streaming architectures, and the challenges that live streaming service providers face in delivering high‐quality, real‐time services. The different techniques used to improve the performance of video streaming, such as adaptive bit‐rate streaming, multicast distribution, and edge computing are discussed and the necessity of low‐latency and high‐quality video transmission in cloud‐based live streaming is underlined. Issues such as improving user experience and live streaming service performance using cutting‐edge technology, like artificial intelligence and machine learning are discussed. In addition, the legal and regulatory implications of cloud‐based live streaming, including issues with network neutrality, data privacy, and content moderation are addressed. The future of cloud computing for live streaming is examined in the section that follows, and it looks at the most likely new developments in terms of trends and technology. For technology vendors, live streaming service providers, and regulators, the findings have major policy‐relevant implications. Suggestions on how stakeholders should address these concerns and take advantage of the potential presented by this rapidly evolving sector, as well as insights into the key challenges and opportunities associated with cloud‐based live streaming are provided.

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STHGN: Citywide Crowd Flow Prediction in Irregular Regions using Hypergraph Convolutional Network

Xing,

Wei

et al. 2023

2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)

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GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content

Cited by 6 publications

References 34 publications

Perceptual video quality assessment: the journey continues!

Perceptual video quality assessment: the journey continues!

Cloud‐based video streaming services: Trends, challenges, and opportunities

STHGN: Citywide Crowd Flow Prediction in Irregular Regions using Hypergraph Convolutional Network

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