Standard Dynamic Range Video Research Articles

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.

Compressed-SDR to HDR Video Reconstruction.

The new generation of organic light emitting diode display is designed to enable the high dynamic range (HDR), going beyond the standard dynamic range (SDR) supported by the traditional display devices. However, a large quantity of videos are still of SDR format. Further, most pre-existing videos are compressed at varying degrees for minimizing the storage and traffic flow demands. To enable movie-going experience on new generation devices, converting the compressed SDR videos to the HDR format (i.e., compressed-SDR to HDR conversion) is in great demands. The key challenge with this new problem is how to solve the intrinsic many-to-many mapping issue. However, without constraining the solution space or simply imitating the inverse camera imaging pipeline in stages, existing SDR-to-HDR methods can not formulate the HDR video generation process explicitly. Besides, they ignore the fact that videos are often compressed. To address these challenges, in this work we propose a novel imaging knowledge-inspired parallel networks (termed as KPNet) for compressed-SDR to HDR (CSDR-to-HDR) video reconstruction. KPNet has two key designs: Knowledge-Inspired Block (KIB) and Information Fusion Module (IFM). Concretely, mathematically formulated using some priors with compressed videos, our conversion from a CSDR-to-HDR video reconstruction is conceptually divided into four synergistic parts: reducing compression artifacts, recovering missing details, adjusting imaging parameters, and reducing image noise. We approximate this process by a compact KIB. To capture richer details, we learn HDR representations with a set of KIBs connected in parallel and fused with the IFM. Extensive evaluations show that our KPNet achieves superior performance over the state-of-the-art methods.

Self-supervised High Dynamic Range Imaging: What Can Be Learned from a Single 8-bit Video?

Recently, Deep Learning-based methods for inverse tone mapping standard dynamic range (SDR) images to obtain high dynamic range (HDR) images have become very popular. These methods manage to fill over-exposed areas convincingly both in terms of details and dynamic range. To be effective, deep learning-based methods need to learn from large datasets and transfer this knowledge to the network weights. In this work, we tackle this problem from a completely different perspective. What can we learn from a single SDR 8-bit video? With the presented self-supervised approach, we show that, in many cases, a single SDR video is sufficient to generate an HDR video of the same quality or better than other state-of-the-art methods.

A Study of Subjective and Objective Quality Assessment of HDR Videos.

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.

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. 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.

A Dataset and Model for the Visual Quality Assessment of Inversely Tone-Mapped HDR Videos.

To enhance the viewer experience of standard dynamic range (SDR) video content on high dynamic range (HDR) displays, inverse tone mapping (ITM) is employed. Objective visual quality assessment (VQA) models are needed for effective evaluation of ITM algorithms. However, there is a lack of specialized VQA models for assessing the visual quality of inversely tone-mapped HDR videos (ITM-HDR-Videos). This paper addresses both an algorithmic and a dataset gap by introducing a novel SDR referenced HDR (SD-R-HD) VQA model tailored for ITM-HDR-Videos, along with the first public dataset specifically constructed for this purpose. The innovations of the SD-R-HD VQA model include 1) utilizing available SDR video as a reference signal, 2) extracting features that characterize standard ITM operations such as global mapping and local compensation, and 3) directly modeling interframe inconsistencies introduced by ITM operations. The newly created ITM-HDR-VQA dataset comprises 200 ITM-HDR-Videos annotated with mean opinion scores, gathered over 320 man-hours of psychovisual experiments. Experimental results demonstrate that the SD-R-HD VQA model significantly outperforms existing state-of-the-art VQA models.

λ-Domain Rate Control via Wavelet-Based Residual Neural Network for VVC HDR Intra Coding.

High dynamic range (HDR) video offers a more realistic visual experience than standard dynamic range (SDR) video, while introducing new challenges to both compression and transmission. Rate control is an effective technology to overcome these challenges, and ensure optimal HDR video delivery. However, the rate control algorithm in the latest video coding standard, versatile video coding (VVC), is tailored to SDR videos, and does not produce well coding results when encoding HDR videos. To address this problem, a data-driven λ-domain rate control algorithm is proposed for VVC HDR intra frames in this paper. First, the coding characteristics of HDR intra coding are analyzed, and a piecewise R-λ model is proposed to accurately determine the correlation between the rate (R) and the Lagrange parameter λ for HDR intra frames. Then, to optimize bit allocation at the coding tree unit (CTU)-level, a wavelet-based residual neural network (WRNN) is developed to accurately predict the parameters of the piecewise R-λ model for each CTU. Third, a large-scale HDR dataset is established for training WRNN, which facilitates the applications of deep learning in HDR intra coding. Extensive experimental results show that our proposed HDR intra frame rate control algorithm achieves superior coding results than the state-of-the-art algorithms. The source code of this work will be released at https://github.com/TJU-Videocoding/WRNN.git.

HDR-BVQM: High dynamic range blind video quality model

Considerable progress has been made toward developing standard dynamic range (SDR) blind video quality assessment (BVQA) models that do not require any baseline reference for quality prediction. However, there is no such method for the high dynamic range (HDR) content. Unlike SDR video, HDR video represents a high-fidelity representation of the real-world scene by preserving the wide luminance range and color gamut. Therefore, SDR BVQA models are not suitable for HDR BVQA. Towards ameliorating this, a first-of-its-kind BVQA model for HDR content is presented in this work. The proposed HDR blind video quality model (HDR-BVQM) is inspired by the spatio-temporal natural scene statistics model, previously employed in SDR blind quality assessment metrics. To build our proposed model, we first develop a comprehensive subjective HDR video quality dataset, including 228 distorted videos generated through three different (H.264, HEVC, packet drop) distortion processes from 19 pristine HDR videos. The developed dataset is then used to extract HDR relevant features, which vary in different distortion types, to train and test the proposed HDR-BVQM. The features are based on the pointwise, pairwise log-derivative, and motion coherence based statistics. Finally, detailed validation and performance comparison is performed with full-reference HDR and no-reference SDR quality assessment methods. The results reveal that the quality prediction by HDR-BVQM correlates with the human judgment of quality.

A New Method for the Simultaneous Creation of Live HDR and SDR Video

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deploying separate high dynamic range (HDR) and standard dynamic range (SDR) production facilities to cover the same event simultaneously can be economically prohibitive. In this case study, we present a new method for creating both ultrahigh-definition (UHD) HDR and HD SDR streams from a single UHD HDR production workflow, developed through a series of real-world UHD HDR production trials in 2018 and 2019, concluding with the 2019 Football Association Challenge Cup (FA Cup), a major U.K. sporting event. We describe the findings from those trials and the lessons learned and include descriptions of what worked well and what worked less well. The workflow solution presented allows for the integration of both HDR camera sources and SDR camera sources (e.g., super slow-motion and specialist cameras), prerecorded or archive SDR content and SDR graphics into a single HDR production environment. It can exploit the backward-compatible nature of the hybrid log-gamma (HLG) HDR solution and recent advances in color volume management. We show how a production’s two outputs, HDR and SDR, can be made using current SDR shading/racking monitoring, with only a single HDR vision guarantee monitor</i> .

A Perception-Based Inverse Tone Mapping Operator for High Dynamic Range Video Applications

The drastic visual improvements introduced by High Dynamic Range (HDR) technologies open new markets for a wide range of industries. Among them, a significant opportunity is offered to owners of legacy Standard Dynamic Range (SDR) content that can be converted to the new standard to take advantage of the enhanced capabilities of the HDR displays. Similarly, since SDR broadcasting infrastructure will continue to be around for the time being, such conversion process is becoming an obvious necessity. To this end, different approaches have tried to efficiently convert SDR images and videos to HDR format, a procedure well known as inverse Tone Mapping. In this paper, we propose a novel high visual quality video inverse Tone Mapping Operator (iTMO) that addresses the inadequacies of the state-of-the-art methods, resulting in high visual quality HDR videos that match the capabilities of the HDR technology. Our approach is based on human visual perception and employs a segmentation method according to the Human Visual System (HVS) sensitivity to brightness changes in different regions of the frame and constructs the mapping curve using the brightness distribution information of these regions. Our iTMO uses a hybrid approach to achieve an optimal balance between the overall contrast and brightness of the output HDR frame by maximizing a weighted sum of contrast and brightness difference between input SDR and generated HDR frame. The proposed iTMO works equally well for all levels of brightness, eliminating any visual artifacts by dynamically maintaining changes at non-perceivable levels. Subjective and objective evaluations validated the superior visual performance of our proposed iTMO over state-of-the-art methods.

Efficient Debanding Filtering for Inverse Tone Mapped High Dynamic Range Videos

When a low or standard dynamic range video is inverse tone mapped to high dynamic range (HDR), there can be banding artifacts in the output HDR video. We design a highly integrated architecture that can detect and alleviate banding artifacts while preserving true edges and details in an extremely efficient way. This is achieved by a 7-tap edge-aware selective sparse filter. Coding artifacts, such as blocky artifacts, can also be reduced by this filter. The filter includes some parameters that depend on the strengths of the banding artifacts. A parameter selection mechanism is presented which considers smoothness of the banding regions and fidelity of the filtering output. The filter yields significant PSNR gain at the regions of artifacts. Subjective tests demonstrate the great quality improvement achieved by the proposed filter, compared to the quality before filtering. The visual quality provided by the filter is better than or similar to that of algorithms which are far more complex.

SR-ITM-GAN: Learning 4K UHD HDR With a Generative Adversarial Network

Currently, high dynamic range (HDR) videos with high resolution (HR) have become popular due to the display and the rendered technological advancements. However, making ultra-high definition (UHD) with HDR videos is expensive. The legacy low-resolution (LR) standard dynamic range (SDR) format is still largely used in practice. It is necessary to search for a solution to transform LR SDR videos into UHD HDR format. In this paper, we consider joint super resolution and learning inverse tone mapping an issue of high-frequency reconstruction and local contrast enhancement, and we propose an architecture based on a generative adversarial network to apply joint SR-ITM learning. Specifically, we include the residual ResNeXt block (RRXB) as a basic module to better capture high-frequency textures and adopt YUV interpolation to achieve local contrast enhancement. By adopting a generative adversarial network as a pivotal training mechanism, our designs show advantages in both integration and performance. Our code is now available on GitHub: SR-ITM-GAN.

Optical-Flow Based Nonlinear Weighted Prediction for SDR and Backward Compatible HDR Video Coding

Tone Mapping Operators (TMO) designed for videos can be classified into two categories. In a first approach, TMOs are temporal filtered to reduce temporal artifacts and provide a Standard Dynamic Range (SDR) content with improved temporal consistency. This however does not improve the SDR coding Rate Distortion (RD) performances. A second approach is to design the TMO with the goal of optimizing the SDR coding rate-distortion performances. This second category of methods may lead to SDR videos altering the artistic intent compared with the produced HDR content. In this paper, we combine the benefits of the two approaches by introducing new Weighted Prediction (WP) methods inside the HEVC SDR codec. As a first step, we demonstrate the interest of the WP methods compared to TMO optimized for RD performances. Then we present the newly introduced WP algorithm and WP modes. The WP algorithm consists in performing a global motion compensation between frames using an optical flow, and the new modes are based on non linear functions in contrast with the literature using only linear functions. The contribution of each novelty is studied independently and in a second time they are all put in competition to maximize the RD performances. Tests were made for HDR backward compatible compression but also for SDR compression only. In both cases, the proposed WP methods improve the RD performances while maintaining the SDR temporal coherency.

Learning-Based Low-Complexity Reverse Tone Mapping With Linear Mapping

Although high dynamic range (HDR) display has become popular recently, the legacy content such as standard dynamic range (SDR) video is still in service and needs to be properly converted on HDR display devices. Therefore, it is desirable for HDR TV sets to have the capability of automatically converting input SDR video into HDR video, which is called reverse tone mapping (RTM). In this paper, we propose a novel learning-based low-complexity RTM scheme that not only expands the suppressed dynamic ranges (DR) of the SDR videos (or images), but also effectively restores lost detail in the SDR videos. Most existing conventional RTM schemes have focused on how to expand the DR of global contrast, resulting in limitations in recovering lost detail of SDR videos. On the other hand, the recent convolutional neural network-based approaches show promising results, but they are too complex to be applied on the users’ devices in practice. In this paper, our learning-based RTM scheme is computationally simple but effective in recovering lost detail. To learn the SDR-to-HDR relation, training “SDR-HDR” images are first separated into their base layer components and detail layer components by applying a guided filter. The detail layer components of the “SDR-HDR” pairs are used to train the SDR-to-HDR mapping. The mapping matrices are computed based on kernel ridge regression. In the meantime, the global contrast of the base layers is expanded by a nonlinear function that suppresses darker regions and amplifies brighter regions to fit the full DR of a target HDR display. To verify the effectiveness of our learning-based RTM scheme, we performed subjective quality assessment for images and videos. The experimental results show that our RTM scheme outperforms the existing RTM scheme with the successful restoration of lost detail in SDR images.

Fast Intra Coding of High Dynamic Range Videos in SHVC

Compared with the conventional standard dynamic range (SDR) content, high dynamic range (HDR) content supplies viewers with more immersive experience by offering a much higher range of luminance. Most of current consumer devices cannot afford to this emerging technology, and content providers decide to create both an HDR version and an SDR version of the same video. In this letter, scalable high efficiency video coding (HEVC) scalable extension of HEVC (SHVC) serves as the coding framework where the base layer (BL) is an 8-b SDR version and the enhancement layer (EL) is a 12-b HDR version. Recently, many fast coding algorithms for SDR videos are proposed, and there is an urgent demand for fast coding algorithms for EL HDR videos. With the coding information of the BL SDR videos, this letter proposes a fast algorithm to reduce the complexity of intra coding for EL HDR videos. First, depth information of neighboring coding tree units (CTUs) in the HDR version and the colocated CTU in the SDR version is used for early coding unit (CU) depth determination. Moreover, four classifiers are trained to predict the CTU depth range. Two classifiers are trained for CTUs in frames with a high average luma, and another two classifiers are used for CTUs in frames with a low average luma. Experimental results show that the proposed algorithm achieves 43% encoding time saving on average, with only a 0.54% Bjøntegaard delta bit rate (BDBR) increase compared to the original SHVC test model.

HDR video quality assessment: Perceptual evaluation of compressed HDR video

Compared with standard dynamic range (SDR) video, the high dynamic range (HDR) video can provide us significantly enhanced viewing experience. In particular, compared to SDR video, the HDR video has better contrast and preserves more details for the same scene. With the rapid development of HDR video compression technology, there is a lack of trusted quality measure of HDR video compression. In order to facilitate the future development of objective HDR quality assessment, we build a HDR video quality assessment database, in which the bitstream is created by compressing a series of HDR video sequences. In the compression, the quantization parameters (QP) are set to 12 levels according to the configuration of the codec. The subjective quality of each bitstream is rated by 22 viewers. It is revealed that the subject viewers have arrived at a reasonable agreement on the subjective quality of different QP levels. This paper presents the results of subjective quality assessment of HDR compressed video, which also exhibits that there is significant room to further improve the objective HDR video quality assessment algorithms.

A Single-Layer HDR Video Coding Framework with SDR Compatibility

The migration from high-definition TV to ultrahigh definition (UHD) is already underway. In addition to an increase of picture spatial resolution, UHD potentially provides more color by introducing a wider color gamut, and better contrast by moving from standard dynamic range (SDR) to high dynamic range (HDR). The transition from SDR to HDR will require distribution solutions supporting some level of SDR backward compatibility. This paper presents the HDR content distribution scheme jointly developed by Technicolor and Philips. The solution is based on a single-layer codec design and provides SDR compatibility, thanks to a preprocessing step applied prior to the encoding. The resulting SDR video can be compressed and distributed, then decoded using standard-compliant decoders (e.g., high efficiency video coding Main 10 compliant) and directly rendered on SDR displays. Dynamic metadata of limited size are used to reconstruct the HDR signal from the decoded SDR video, using a post-processing that is the functional inverse of the preprocessing. Both HDR quality and artistic intent are preserved. Pre- and postprocessing are applied independently per picture, do not involve any interpixel dependence, and are codec agnostic.

HDR와 SDR 비디오의 스케일러블 부호화를 위한 계층 압축 기법

In this paper, we propose a scalable coding method for high dynamic range (HDR) and standard dynamic range (SDR) videos based on Scalable High Efficiency Video Coding (SHVC). The proposed method has multi-layer coding architecture that consists of base layer for SDR videos and enhancement layer for HDR videos to support the backward compatibility with legacy codec and display devices. Also, to improve coding efficiency of enhancement layers, a global inverse tone mapping is applied to the reconstructed SDR video and the compensated frames are referred for coding of the enhancement layer. The proposed method is found to achieve BD-Rate gain of 43.0% on average (maximum 76.3%) for the enhancement layer and 15.7% on average (maximum 31%) for dual-layer against the SHM 7.0 reference software.