Multiview streaming is increasingly used in sports and entertainment, but current solutions struggle to support adaptive bitrate (ABR) streaming and robustness on consumer devices that expose only a single hardware HEVC decoder. Existing HEVC bitstream stitching and cloud-mixing methods [1], [2] can merge multiple streams into one, but they are fragile when one input stream is lost. They also typically assume a fixed set of view resolutions and a static layout, so if ABR logic tries to change the resolution of one view independently, the resulting arrangement may no longer form a valid rectangular HEVC picture, making per-view ABR difficult to support.

In the rapidly growing streaming service market, including satellite options, Internet Service Providers (ISPs) face the challenge of continually optimizing network performance to deliver superior video streaming quality, which is vital to optimize customer satisfaction. This pressing need has sparked a drive towards developing advanced Quality of Experience (QoE) prediction models, which are essential in enhancing streaming protocols and guaranteeing smooth viewing experiences for users. However, the efficacy of these models hinges on the availability of extensive, diverse datasets. To fill this critical data void, our study introduces the publicly available LIVE-Viasat Real-World Satellite QoE Database, with 179 videos from real-world streaming, encompassing a range of distortions. Enhanced by a study with 54 participants providing detailed QoE feedback, our work not only provides a rich analysis of the determinants of subjective QoE but also delves into how various streaming impairments influence user behavior, thereby offering a more holistic understanding of user satisfaction.

In an adaptive bitrate streaming application, the efficiency of video compression and the encoded video quality depend on both the video codec and the quality metric used to perform encoding optimization. The development of such a quality metric need large scale subjective datasets. In this work we merge several datasets into one to support the creation of a metric tailored for video compression and scaling. We proposed a set of HEVC lightweight features to boost performance of the metrics. Our metrics can be computed from tightly coupled encoding process with 4% compute overhead or from the decoding process in real-time. The proposed method can achieve better correlation than VMAF and P.1204.3. It can extrapolate to different dynamic ranges, and is suitable for real-time video quality metrics delivery in the bitstream. The performance is verified by in-distribution and cross-dataset tests. This work paves the way for adaptive client-side heuristics, real-time segment optimization, dynamic bitrate capping, and quality-dependent post-processing neural network switching, etc.

We conducted a large-scale study of human perceptual quality judgments of High Dynamic Range (HDR) and Standard Dynamic Range (SDR) videos subjected to scaling and compression levels and viewed on three different display devices. While conventional expectations are that HDR quality is better than SDR quality, we have found subject preference of HDR versus SDR depends heavily on the display device, as well as on resolution scaling and bitrate. To study this question, we collected more than 23,000 quality ratings from 67 volunteers who watched 356 videos on OLED, QLED, and LCD televisions, and among many other findings, observed that HDR videos were often rated as lower quality than SDR videos at lower bitrates, particularly when viewed on LCD and QLED displays. Since it is of interest to be able to measure the quality of videos under these scenarios, e.g. to inform decisions regarding scaling, compression, and SDR vs HDR, we tested several well-known full-reference and no-reference video quality models on the new database. Towards advancing progress on this problem, we also developed a novel no-reference model called HDRPatchMAX, that uses a contrast-based analysis of classical and bit-depth features to predict quality more accurately than existing metrics.

As compared to standard dynamic range (SDR) videos, high dynamic range (HDR) content is able to represent and display much wider and more accurate ranges of brightness and color, leading to more engaging and enjoyable visual experiences. HDR also implies increases in data volume, further challenging existing limits on bandwidth consumption and on the quality of delivered content. Perceptual quality models are used to monitor and control the compression of streamed SDR content. A similar strategy should be useful for HDR content, yet there has been limited work on building HDR video quality assessment (VQA) algorithms. One reason for this is a scarcity of high-quality HDR VQA databases representative of contemporary HDR standards. Towards filling this gap, we created the first publicly available HDR VQA database dedicated to HDR10 videos, called the Laboratory for Image and Video Engineering (LIVE) HDR Database. It comprises 310 videos from 31 distinct source sequences processed by ten different compression and resolution combinations, simulating bitrate ladders used by the streaming industry. We used this data to conduct a subjective quality study, gathering more than 20,000 human quality judgments under two different illumination conditions. To demonstrate the usefulness of this new psychometric data resource, we also designed a new framework for creating HDR quality sensitive features, using a nonlinear transform to emphasize distortions occurring in spatial portions of videos that are enhanced by HDR, e.g., having darker blacks and brighter whites. We apply this new method, which we call HDRMAX, to modify the widely-deployed Video Multimethod Assessment Fusion (VMAF) model. We show that VMAF+HDRMAX provides significantly elevated performance on both HDR and SDR videos, exceeding prior state-of-the-art model performance. The database is now accessible at: https://live.ece.utexas.edu/research/LIVEHDR/LIVEHDR_index.html. The model will be made available at a later date at: https://live.ece.utexas.edu//research/Quality/index_algorithms.htm.

High Dynamic Range (HDR) videos can represent a much greater range of brightness and color than Standard Dynamic Range (SDR) videos and are rapidly becoming an industry standard. HDR videos have more challenging capture, transmission, and display requirements than legacy SDR videos. With their greater bit depth, advanced electro-optical transfer functions, and wider color gamuts, comes the need for video quality algorithms that are specifically designed to predict the quality of HDR videos. Towards this end, we present the first publicly released large-scale subjective study of HDR videos. We study the effect of distortions such as compression and aliasing on the quality of HDR videos. We also study the effect of ambient illumination on perceptual quality of HDR videos by conducting the study in both a dark lab environment and a brighter living-room environment. A total of 66 subjects participated in the study and more than 20,000 opinion scores were collected, which makes this the largest in-lab study of HDR video quality ever. We anticipate that the dataset will be a valuable resource for researchers to develop better models of perceptual quality for HDR videos.

Video livestreaming is gaining prevalence among video streaming service s, especially for the delivery of live, high motion content such as sport ing events. The quality of the se livestreaming videos can be adversely affected by any of a wide variety of events, including capture artifacts, and distortions incurred during coding and transmission. High motion content can cause or exacerbate many kinds of distortion, such as motion blur and stutter. Because of this, the development of objective Video Quality Assessment (VQA) algorithms that can predict the perceptual quality of high motion, live streamed videos is greatly desired. Important resources for developing these algorithms are appropriate databases that exemplify the kinds of live streaming video distortions encountered in practice. Towards making progress in this direction, we built a video quality database specifically designed for live streaming VQA research. The new video database is called the Laboratory for Image and Video Engineering (LIVE) Livestream Database. The LIVE Livestream Database includes 315 videos of 45 source sequences from 33 original contents impaired by 6 types of distortions. We also performed a subjective quality study using the new database, whereby more than 12,000 human opinions were gathered from 40 subjects. We demonstrate the usefulness of the new resource by performing a holistic evaluation of the performance of current state-of-the-art (SOTA) VQA models. We envision that researchers will find the dataset to be useful for the development, testing, and comparison of future VQA models. The LIVE Livestream database is being made publicly available for these purposes at https://live.ece. utexas.edu/research/LIVE_APV_Study/apv_index.html.

Video live streaming is gaining prevalence among video streaming services, especially for the delivery of popular sporting events. Many objective Video Quality Assessment (VQA) models have been developed to predict the perceptual quality of videos. Appropriate databases that exemplify the distortions encountered in live streaming videos are important to designing and learning objective VQA models. Towards making progress in this direction, we built a video quality database specifically designed for live streaming VQA research. The new video database is called the Laboratory for Image and Video Engineering (LIVE) Live stream Database. The LIVE Livestream Database includes 315 videos of 45 contents impaired by 6 types of distortions. We also performed a subjective quality study using the new database, whereby more than 12,000 human opinions were gathered from 40 subjects. We demonstrate the usefulness of the new resource by performing a holistic evaluation of the performance of current state-of-the-art (SOTA) VQA models. The LIVE Livestream database is being made publicly available for these purposes at https://live.ece.utexas.edu/research/LIVE_APV_Study/apv_index.html.

We propose a new model for no-reference video quality assessment (VQA). Our approach uses a new idea of highly-localized space-time (ST) slices called Space-Time Chips (ST Chips). ST Chips are localized cuts of video data along directions that implicitly capture motion. We use perceptually-motivated bandpass and normalization models to first process the video data, and then select oriented ST Chips based on how closely they fit parametric models of natural video statistics. We show that the parameters that describe these statistics can be used to reliably predict the quality of videos, without the need for a reference video. The proposed method implicitly models ST video naturalness, and deviations from naturalness. We train and test our model on several large VQA databases, and show that our model achieves state-of-the-art performance at reduced cost, without requiring motion computation.

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.

We introduce a novel feature set, which we call HDRMAX features, that when included into Video Quality Assessment (VQA) algorithms designed for Standard Dynamic Range (SDR) videos, sensitizes them to distortions of High Dynamic Range (HDR) videos that are inadequately accounted for by these algorithms. While these features are not specific to HDR, and also augment the equality prediction performances of VQA models on SDR content, they are especially effective on HDR. HDRMAX features modify powerful priors drawn from Natural Video Statistics (NVS) models by enhancing their measurability where they visually impact the brightest and darkest local portions of videos, thereby capturing distortions that are often poorly accounted for by existing VQA models. As a demonstration of the efficacy of our approach, we show that, while current state-of-the-art VQA models perform poorly on 10-bit HDR databases, their performances are greatly improved by the inclusion of HDRMAX features when tested on HDR and 10-bit distorted videos.