Video quality

About: Video quality is a research topic. Over the lifetime, 13143 publications have been published within this topic receiving 178307 citations.

Automatic prediction of perceptual quality of multimedia signals--a survey

Abstract: We survey recent developments in multimedia signal quality assessment, including image, audio, video, and combined signals. Such an overview is timely given the recent explosion in all-digital sensory entertainment and communication devices pervading the consumer space. Owing to the sensory nature of these signals, perceptual models lie at the heart of multimedia signal quality assessment algorithms. We survey these models and recent competitive algorithms and discuss comparison studies that others have conducted. In this context we also describe existing signal quality assessment databases. We envision that the reader will gain a firmer understanding of the broad topic of multimedia quality assessment, of the various sub-disciplines corresponding to different signal types, how these signals types co-relate in producing an overall user experience, and what directions of research remain to be pursued.

Error control for video multicast using hierarchical FEC

Abstract: Bit-rate scalable video compression with layered multicast has been shown to be an effective method to achieve rate control in heterogeneous networks. We further propose the use of hierarchical FEC as an error control mechanism that allows receivers to individually trade-off latency for received video quality. The scheme is efficient since FEC packets are used to protect only the more important data layers and is multicast only to receivers that need them, thereby improving network utilization. Furthermore, there is no loss in error correcting capability by using hierarchical FEC when maximum distance separable codes are used. Actual MBONE experiments are performed to evaluate the performance of the proposed scheme.

Advanced Performance Achievement using Multi- Algorithmic Approach of Video Transcoder for Low Bitrate Wireless Communication

Abstract: This paper presents the different issue of video streaming algorithms without compromising the video quality in the distributed environment. Our theme of this research is to manage the critical processing stages (speed, information loss, redundancy and error resilience) having better encoded ratio, without the fluctuation of quantization scale by using IP configuration. In this paper, we implement different techniques such as upgraded features of the Motion Compensation with Discrete Cosine Transform (MCDCT) domain criteria regarded with prediction error and Motion Vector (MV) at low-frequency coefficient, while control of frame resolution having downscale of future prediction error with lower transmission bitrate in this streaming media. However, delay of bits in encoded buffer side is being controlled to produce the video with high quality and maintenance a low buffering delay. Our results show the performance accuracy gain with better achievement than a number of previous approaches in all the above processes in an encouraging mode.

Shedding light on the structure of internet video quality problems in the wild

Abstract: The key role that video quality plays in impacting user engagement, and consequently providers' revenues, has motivated recent efforts in improving the quality of Internet video. This includes work on adaptive bitrate selection, multi-CDN optimization, and global control plane architectures. Before we embark on deploying these designs, we need to first understand the nature of video of quality problems to see if this complexity is necessary, and if simpler approaches can yield comparable benefits. To this end, this paper is a first attempt to shed some light on the structure of video quality problems. Using measurements from 300 million video sessions over a two-week period, we identify recurrent problems using a hierarchical clustering approach over the space of client/session attributes (e.g., CDN, AS, connectivity). Our key findings are that: (1) a small number (2%) of critical clusters account for 83% of join failure problems (44--84% for other metrics); (2) many problem events (50%) persist for at least 2 hours; (3) a majority of these problems (e.g., 60% of join failures, 30--60% for other metrics) are related to content provider, CDN, or client ISP issues. Building on these insights, we evaluate the potential improvement by focusing on addressing these recurrent problems and find that fixing just 1% of these clusters can reduce the number of problematic sessions by 55% for join failures (15%--40% for other metrics).

NR-GVQM: A No Reference Gaming Video Quality Metric

Abstract: Gaming as a popular system has recently expanded the associated services, by stepping into live streaming services. Live gaming video streaming is not only limited to cloud gaming services, such as Geforce Now, but also include passive streaming, where the players' gameplay is streamed both live and ondemand over services such as Twitch.tv and YouTubeGaming. So far, in terms of gaming video quality assessment, typical video quality assessment methods have been used. However, their performance remains quite unsatisfactory. In this paper, we present a new No Reference (NR) gaming video quality metric called NR-GVQM with performance comparable to state-of-the-art Full Reference (FR) metrics. NR-GVQM is designed by training a Support Vector Regression (SVR) with the Gaussian kernel using nine frame-level indexes such as naturalness and blockiness as input features and Video Multimethod Assessment Fusion (VMAF) scores as the ground truth. Our results based on a publicly available dataset of gaming videos are shown to have a correlation score of 0.98 with VMAF and 0.89 with MOS scores. We further present two approaches to reduce computational complexity.