Subjective and objective quality assessment for advanced videos

The surge of video streaming services, particularly for high motion content such as sporting events, necessitates advanced techniques to maintain video quality, facing challenges such as capture artifacts and distortions during coding and transmission. The advent of High Dynamic Range (HDR) content, offering a broader and more accurate representation of brightness and color, poses additional complexities due to increased data volume. The critical need for robust Video Quality Assessment (VQA) models arises from these challenges. To meet this need, we conducted three substantial subjective quality studies and constructed corresponding databases. The Laboratory for Image and Video Engineering (LIVE) Livestream Database comprises 315 videos of 45 source sequences from 33 original contents impaired by six types of distortions. This database facilitated the gathering of over 12,000 human opinions from 40 subjects. The LIVE HDR Database, the first of its kind dedicated to HDR10 videos, includes 310 videos from 31 distinct source sequences, processed with ten different compression and resolution combinations. This resource was instrumental in amassing over 20,000 human quality judgments under two different illumination conditions. An additional LIVE HDR AQ was developed with 400 videos from 40 unique source sequences. These videos were processed using varied compression, resolution combinations, and AQ-mode settings, to study the effects of adaptive quantization (AQ) and rate-distortion optimization techniques on HDR video perceptual quality. Building on these invaluable databases, we developed two innovative objective quality models: HDRMAX and HDRGREED. HDRMAX, a pioneering framework designed to create HDR quality-sensitive features, augments the widely-deployed Video Multimethod Assessment Fusion (VMAF) model, yielding significantly improved performance on both HDR and SDR videos. HDRGREED, a novel model leveraging localized histogram equalization and Difference of Gaussian filters, employs the Generalized Gaussian Distribution to model the bandpass responses and measure the entropy variations between reference and distorted videos. This model is particularly sensitive to banding and blocking artifacts introduced by inappropriate AQ settings. In conclusion, the comprehensive subjective quality studies and databases, along with the state-of-the-art objective quality models, HDRMAX and HDRGREED, significantly contribute to the advancement of future VQA models. These tools cater specifically to challenges posed by live streaming and HDR content, providing critical resources for the development, testing, and comparison of future VQA models. These databases, publicly available for research purposes, and the innovative models offer valuable insights to improve and control the perceptual quality of streamed videos.Electrical and Computer Engineerin

HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos

We present a no-reference video quality model and algorithm that delivers standout performance for High Dynamic Range (HDR) videos, which we call HDR-ChipQA. HDR videos represent wider ranges of luminances, details, and colors than Standard Dynamic Range (SDR) videos. The growing adoption of HDR in massively scaled video networks has driven the need for video quality assessment (VQA) algorithms that better account for distortions on HDR content. In particular, standard VQA models may fail to capture conspicuous distortions at the extreme ends of the dynamic range, because the features that drive them may be dominated by distortions {that pervade the mid-ranges of the signal}. We introduce a new approach whereby a local expansive nonlinearity emphasizes distortions occurring at the higher and lower ends of the {local} luma range, allowing for the definition of additional quality-aware features that are computed along a separate path. These features are not HDR-specific, and also improve VQA on SDR video contents, albeit to a reduced degree. We show that this preprocessing step significantly boosts the power of distortion-sensitive natural video statistics (NVS) features when used to predict the quality of HDR content. In similar manner, we separately compute novel wide-gamut color features using the same nonlinear processing steps. We have found that our model significantly outperforms SDR VQA algorithms on the only publicly available, comprehensive HDR database, while also attaining state-of-the-art performance on SDR content

Making Video Quality Assessment Models Robust to Bit Depth

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.Comment: Published in IEEE Signal Processing Letters 202

HDR or SDR? A Subjective and Objective Study of Scaled and Compressed Videos

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. HDR videos are able to present wider color gamuts, better contrasts, and brighter whites and darker blacks than SDR videos. 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. 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 both classical and bit-depth sensitive distortion statistics more accurately than existing metrics

Perceptual video quality assessment: the journey continues!

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