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.

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.

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.

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 present a new method and a large-scale database to detect audio-video synchronization(A/V sync) errors in tennis videos. A deep network is trained to detect the visual signature of the tennis ball being hit by the racquet in the video stream. Another deep network is trained to detect the auditory signature of the same event in the audio stream. During evaluation, the audio stream is searched by the audio network for the audio event of the ball being hit. If the event is found in audio, the neighboring interval in video is searched for the corresponding visual signature. If the event is not found in the video stream but is found in the audio stream, A/V sync error is flagged. We developed a large-scaled database of 504,300 frames from 6 hours of videos of tennis events, simulated A/V sync errors, and found our method achieves high accuracy on the task.

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 present a method to restore a clear image from a haze-affected image using a Wasserstein generative adversarial network. As the problem is ill-conditioned, previous methods have required a prior on natural images or multiple images of the same scene. We train a generative adversarial network to learn the probability distribution of clear images conditioned on the haze-affected images using the Wasserstein loss function, using a gradient penalty to enforce the Lipschitz constraint. The method is data-adaptive, end-to-end, and requires no further processing or tuning of parameters. We also incorporate the use of a texturebased loss metric and the L1 loss to improve results, and show that our results are better than the current state-of-the-art.