Low Bit Research Articles

An innovative image encryption algorithm enhanced with the Pan-Tompkins Algorithm for optimal security

This study introduces a cutting-edge image encryption algorithm aimed at elevating security standards. The Pan-Tompkins Algorithm (PTA) for key generation is proposed for the first time in this study. Additionally, employing steganography through the Least Significant Bit (LSB) method for embedding keys within the encrypted image enhances secure key distribution, thereby fortifying the encryption process. On the other hand, the integration of advanced algorithms, such as Zigzag scanning, the Affine Image Encryption Algorithm (AA), and the Vigenere Image Encryption Algorithm (VA), constitutes the fundamental innovation of the proposed image encryption algorithm. The proposed algorithm is named PanAAVA:Affine Algorithm and Vigenere Algorithm Encryption with PTA-Based Key Generation. The PanAAVA algorithm ensures unparalleled security by encrypting the positions and values of pixels using AA and VA. Notably, using PTA for key generation marks a distinctive and new key generation method feature of the algorithm. To assess the effectiveness of the PanAAVA, a comprehensive comparative analysis is conducted against well-established encryption methodologies, including Lena, Baboon, Airplane, and Pepper.The PanAAVA demonstrates exceptional proficiency in histogram analysis. The PanAAVA demonstrates a Unified Average Changing Intensity (UACI) of 33.4044%. Additionally, the Number of Pixels Change Rate (NPCR) is measured at 99.7442%, showcasing the algorithm’s effectiveness in inducing significant pixel changes. The proposed algorithm’s Mean Square Error (MSE) is calculated at 3.20679E5%. The proposed algorithm’s Peak Signal to Noise Ratio (PSNR) is recorded at 9.512475. The Key Space Size of the proposed algorithm is measured at 2209. Regarding correlation analysis, the PanAAVA achieves a high correlation score of 7.9996. The proposed algorithm successfully passes the National Institute of Standards and Technology (NIST) analysis, demonstrating a remarkably strong correlation close to 0 and a Structural Similarity Index Measure (SSIM) of 0.9977. Furthermore, regarding quantum communication, the proposed algorithm maintains stable key rates of 47.5 ± 0.8 kHz during the day and 50.9 ± 0.7 kHz at night. Additionally, PanAAVA achieves low Quantum Bit Error Rate (QBER) values of 4.77 ± 0.02, ensuring reliable and secure communication. The PanAAVA also demonstrates robust asymmetries at 49.81 ± 0.02 and 50.14 ± 0.03 for a crystal length of 20 mm. highlighting PanAAVA’s adaptability and effectiveness in different scenarios. PanAAVA outperforms other encryption algorithms concerning performance measurements and comparisons. In conclusion, the PanAAVA emerges as a beacon of superior security capabilities and innovation in image encryption, showcasing the potential to redefine standards in the field.

SCFI: Efficient forward fine-grained control flow integrity based on coarse-grained ISA extensions

Code reuse attack (CRA) is a severe threat to computer systems. To mitigate this threat, control-flow integrity (CFI) was proposed to restrict control-flow transfers. Processor vendors developed Instruction Set Architecture (ISA) extensions to enforce forward CFI on processors, such as Indirect Branch Tracking (IBT) developed by Intel and Branch Target Identification (BTI) developed by ARM. However, these extensions are coarse-grained and unable to provide enough security protection.We combine existing coarse-grained ISA extension with novel software modifications and propose a new fine-grained forward CFI implementation called Slot-based CFI (SCFI). SCFI consists of two software modifications: Slot Policy and ID Policy. Slot policy utilises lower bits of code address as labels to implement fine-grained CFI. ID Policy provides multi-label support to solve the destination equivalence problem. SCFI strengthens security by improving coarse-grained ISA extension to fine-grained CFI (improving Average Indirect Targets Allowed (AIA) from 752 to 6.24). SCFI costs negligible performance overhead (on average 0.78% in SPEC2006). It also has good performance in complex programs (1.12% in 471.omnetpp and 1.79% in 483.xalancbmk). In addition, SCFI only needs lightweight software instrument without hardware modification and supports complex control-flow graphs.

Optimization Method of RTOS System Delay Scheduling Based on Multi-Core Processor

In order to improve the task execution efficiency of multi-core processor, this paper studies the system delay scheduling optimization method of real-time operating system based on multi-core processor. Firstly, the core structure analysis model of multi-core processor data fusion and data acquisition in RTOS system is established, and then the task information flow of delay scheduling in RTOS system is constructed by using the correlation dimension feature fusion method. Based on the power domain non orthogonal multiple access control method, the data fusion state factor of multi-core processor is calculated for the initial scheduling node, and multi-core processor is used to control load balancing in the delay balancing scheduling process of RTOS system, The delay scheduling and balance control of real-time operating system are realized by multi-core processor. Simulation results show that the algorithm has low bit error rate and low task overhead in RTOS system, and can improve the task execution efficiency of multi-core processor.

Optimizing elastic optical networks quality of transmission with an empirical optical amplifier gain saturation model

In this paper, we revisit the problem of adjusting transceiver power, amplifier gain, and nodal gain in optical-bypass-enabled networks, taking into account an empirical model of erbium-doped fiber amplifier (EDFA) gain saturation. We assume that network demands have been routed via the shortest possible paths. A heuristic algorithm, which estimates the degradation of the signal-to-noise ratio (SNR) for each demand, is used to assign the modulation level and spectrum slots to the routed demands. Our optimization process aims to maximize the minimum SNR margins of demands, by adjusting EDFA small-signal gain, variable optical attenuator (VOA) attenuation, and transceiver power. Constraints and impairments imposed by the physical-layer include EDFA and VOA gain limits, transceiver power limits, receiver sensitivity, insertion loss of optical switch input ports, EDFA gain saturation, EDFA noise, and fiber non-linear interference. These are modeled using the Gaussian noise model. The proposed optimization problem is formulated as a geometric programming problem and then converted into an equivalent convex problem.Numerical results suggest that in the optimal solution of our optimization, all demands are served with typically low bit error rates (BER) (<10−4). We calculate the operational gains of the EDFA to set the spans to full compensation whenever possible. Pre-amplifiers and line amplifiers do not experience gain saturation due to relatively small input powers, while approximately 10% of boosters undergo more than a 1 dB gain reduction due to high input powers. We observe that overlooking the effect of EDFA gain saturation and physical-layer-imposed constraints such as nodal gain limits or transceiver power limits during network optimization leads to a considerable number of blocked demands due to SNR degradation or high BER (>10−3).

Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks.

The field of wireless communication is currently being pushed to new boundaries with the emergence of 6G technology. This advanced technology requires substantially increased data rates and processing speeds while simultaneously requiring energy-efficient solutions for real-world practicality. In this work, we apply a neuroscience-inspired machine learning model called echo state network (ESN) to the critical task of symbol detection in massive MIMO-OFDM systems, a key technology for 6G networks. Our work encompasses the design of a hardware-accelerated reservoir neuron architecture to speed up the ESN-based symbol detector. The design is then validated through a proof of concept on the Xilinx Virtex-7 FPGA board in real-world scenarios. The experiment results show the great performance and scalability of our symbol detector design across a range of MIMO configurations, compared with traditional MIMO symbol detection methods like linear minimum mean square error. Our findings also confirm the performance and feasibility of our entire system, reflected in low bit error rates, low resource utilization, and high throughput.

Entanglement-based quantum digital signatures over a deployed campus network.

The quantum digital signature protocol offers a replacement for most aspects of public-key digital signatures ubiquitous in today's digital world. A major advantage of a quantum-digital-signatures protocol is that it can have information-theoretic security, whereas public-key cryptography cannot. Here we demonstrate and characterize hardware to implement entanglement-based quantum digital signatures over our campus network. Over 25 hours, we collect measurements on our campus network, where we measure sufficiently low quantum bit error rates (<5% in most cases) which in principle enable quantum digital signatures at over 50 km as shown through rigorous simulation accompanied by a noise model developed specifically for our implementation. These results show quantum digital signatures can be successfully employed over deployed fiber. Moreover, our reported method provides great flexibility in the number of users, but with reduced entanglement rate per user. Finally, while the current implementation of our entanglement-based approach has a low signature rate, feasible upgrades would significantly increase the signature rate.

Performance analysis of multiview video compression based on MIV and VVC multilayer

AbstractTo represent immersive media providing six degree‐of‐freedom experience, moving picture experts group (MPEG) immersive video (MIV) was developed to compress multiview videos. Meanwhile, the state‐of‐the‐art versatile video coding (VVC) also supports multilayer (ML) functionality, enabling the coding of multiview videos. In this study, we designed experimental conditions to assess the performance of these two state‐of‐the‐art standards in terms of objective and subjective quality. We observe that their performances are highly dependent on the conditions of the input source, such as the camera arrangement and the ratio of input views to all views. VVC‐ML is efficient when the input source is captured by a planar camera arrangement and many input views are used. Conversely, MIV outperforms VVC‐ML when the camera arrangement is non‐planar and the ratio of input views to all views is low. In terms of the subjective quality of the synthesized view, VVC‐ML causes severe rendering artifacts such as holes when occluded regions exist among the input views, whereas MIV reconstructs the occluded regions correctly but induces rendering artifacts with rectangular shapes at low bitrates.

Shear-interference assisted deep-learning for enhancing spatially multiplexing capacity of free-space communication

The information capacity is a critical measure in free-space optical (FSO) communication. Traditional mode-multiplexing techniques employed in optical communication utilizing Laguerre-Gaussian beams (LGBs) necessitate proper topological charge interval to prevent mode-crosstalk. In this paper, we propose a novel approach that integrates deep learning (DL) into the LGB-based optical communication model, incorporating shearing interference techniques. This combination enables the FSO system to achieve the theoretical limit of capacity, resulting in a remarkable 300 % increase in information capacity compared to traditional techniques. Experimental results demonstrate that the constructed neural network attains a flawless pattern recognition accuracy of 100 %. Additionally, we evaluate various partially coherent light scenarios under the influence of beam decoherence. The results reveal that accurate discrimination of multiplexing patterns is maintained when the fringe contrast exceeds 0.43. Even at a degraded contrast level of 0.18, the recognition accuracy remains impressively high at 91.16 %. To demonstrate practicality, we successfully encode and transmit a cyan-magenta-yellow-black (CMYK) color image using the proposed system, achieving a low bit error rate of 7.5 × 10-5. These findings highlight the potential of DL-assisted mode-division multiplexing for future optical communication applications.

MieWC: Medical image encryption using wavelet transform and multiple chaotic maps

AbstractRecently, digital images have been widely used in several applications through advanced wearable devices and networks. Despite several benefits of digital images, such as easy distribution, storage, and reproduction, it is difficult to prevent the issue of identity theft, privacy leakage, and ownership conflicts. We present a wavelet‐based encryption technique, MieWC, to solve the above issues using multiple chaotic keys. This work uses multiple keys for the encryption process, which ensures high security and authenticity. Lorenz and logistic maps are used for chaotic key generation for diffusion and confusion processes. Further, the technique is tested for two different wavelet transforms, including integer wavelet transform (IWT) and discrete wavelet transform, with IWT giving better results. Hence, it is being used for further analysis. The proposed technique is easy to implement and provides high security with improved results. It has low time complexity and high key bit sensitivity. The result analysis further ensures resistance against crop attacks.

Performance evaluation of mpeg-5 part 2 (lcevc): impact of packet loss

This paper presents an evaluation of the latest MPEG-5 Part 2 Low Complexity Enhancement Video Coding (LCEVC) for video streaming applications using best effort protocols. LCEVC is a new video standard by MPEG, which enhances any base codec through an additional low bitrate stream, improving both video compression efficiency and and transmission. However, there is an interplay between packetization, packet loss visibility, choice of codec and video quality, which implies that prior studies with other codecs may be not as relevant. The contributions of this paper is, therefore in twofold: It evaluates the compression performance of LCEVC and then the impact of packet loss on its video quality when compared to H.264 and HEVC.The results from this evaluation suggest that, regarding compression, LCEVC outperformed its base codecs, overall in terms average encoding bitrate savings when using the constant rate factor (CRF) rate control. For example at a CRF of 19, the average encoding bitrate was reduced by 18.7% and 15.8% when compared with the base H.264 and HEVC codecs respectively. Furthermore, LCEVC produced better visual quality across the packet loss range compared to its base codecs and the quality only started to decrease once packet loss exceeded 0.8-1%, and decreases at a slower pace compared to its equivalent base codecs. This suggests that the LCEVC enhancement layer also provides error concealment. The results presented in this paper will be of interest to those considering the LCEVC standard and expected video quality in error-prone environments

Ultra-Bend-Resistant 4-Core Simplex Cable Used for Short-Reach Dense Spatial Division Multiplexing Optical Transmission.

We optimized and fabricated an ultra-bend-resistant 4-core simplex cable (SXC) employing 4-core multicore fiber (MCF) suitable for short-reach dense spatial division multiplexing (DSDM) optical transmission in the O-band. The characteristics of transmission loss, macro-bending and cross-talk (XT) between adjacent cores after cabling were firstly clarified. By introducing the trapezoid index and optimizing the cabling process, the maximum values of added XT of 1.17 dB/km due to 10 loops with a bending radius of 6 mm imposed over the 4-core SXC and a macro-bending loss of 0.37 dB/10 turns were, respectively, achieved.P Then, the optical transmission with low bit error rate (BER) was presented using a 100GBASE-LR4 transceiver over the 1.2 km long 4-core SXC. The excellent bending resistance of the 4-core SXC may pave the way for a reduction in space pressure and increase in access density on short-reach optical interconnect (OI) based on DSDM.

A New Method for Intelligent Multimedia Compression Based on Discrete Hartley Matrix

Multimedia data (video, audio, images) require storage space and transmission bandwidth when sent through social media networking. Despite rapid advances in the capabilities of digital communication systems, the high data size and data transfer bandwidth continue to exceed the capabilities of available technology, especially among social media users. The recent growth of multimedia-based web applications such as WhatsApp, Telegram, and Messenger has created a need for more efficient ways to compress media data. This is because the transmission speed of networks for multimedia data is relatively slow. In addition, there is a specific size for sending files via email or social networks, because much high-definition multimedia information can reach the Giga Byte size. Moreover, most smart cameras have high imaging resolution, which increases the bit rate of multimedia files of video, audio, and image. Therefore, the goal of data compression is to represent media (video, audio, images, etc.) as accurately as possible with the minimum number of bits (bit rate). Traditional data compression methods are complex for users. They require a high processing power for media data. This shows that most of the existing algorithms have loss in data during the process of compressing and decompressing data, with a high bitrate for media data (video, audio, and image). Therefore, this work describes a new method for media compression systems by discrete Hartley matrix (128) to get a high speed and low bit rate for compressing multimedia data. Finally, the results show that the proposed algorithm has a high-performance speed with a low bit rate for compression data, without losing any part of data (video, sound, and image). Furthermore, the majority of users of social media are satisfied with the data compression interactive system, with high performance and effectiveness in compressing multimedia data. This, in turn, will make it easier for users to easily send their files of video, audio, and images via social media networks.

Facial Image Compression via Neural Image Manifold Compression

Although the recent learning-based image and video coding techniques achieve rapid development, the signal fidelity-driven target in these methods leads to the divergence to a highly effective and efficient coding framework for both human and machine. In this paper, we aim to address the issue by making use of the power of generative models to bridge the gap between full fidelity (for human vision) and high discrimination (for machine vision). Therefore, relying on existing pretrained generative adversarial networks (GAN), we build a GAN inversion framework that projects the image into a low-dimensional natural image manifold. In this manifold, the feature is highly discriminative and also encodes the appearance information of the image, named as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">latent code</i> . Taking a variational bit-rate constraint with a hyperprior model to model/suppress the entropy of image manifold code, our method is capable of fulfilling the needs of both machine and human visions at very low bit-rates. To improve the visual quality of image reconstruction, we further propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple latent codes</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scalable inversion</i> . The former gets several latent codes in the inversion, while the latter additionally compresses and transmits a shallow compact feature to support visual reconstruction. Experimental results demonstrate the superiority of our method in both human vision tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> . image reconstruction, and machine vision tasks, including semantic parsing and attribute prediction.

Simulation of all‐optical NOR gate design with improved extinction ratio using semiconductor optical amplifier

AbstractOptical computers, leveraging the advantages of light characteristics, hold the potential to outperform electronic computers significantly. The role of all‐optical logic gates is pivotal in realizing such high‐performance computing systems. This research introduces a streamlined design for an all‐optical NOR logic gate utilizing a semiconductor optical amplifier. The proposed gate operates at a remarkable data rate of 40 Gbps, achieving an excellent extinction ratio of 20.285 dB. Notably, the design exhibits very low dispersion (−0.029 × 10+006 ps/nm) and an impressively low bit error rate of 4.27 × 10−26.

Evaluation of FBMC Channel Estimation using multiple Auxiliary symbols for high throughput and low BER 5G and beyond communications

Filter Bank Multi-Carrier (FBMC) modulation has been recognized as a consistent contender and a possible successor for Orthogonal Frequency Division Multiplexing (OFDM) in 5G and beyond because of its outstanding spectral properties. The channel is assessed in FBMC using pilot-symbol aided channel estimation that provides robust estimates even for severe channel conditions. In the present work, neutraliz ing the imaginary interference at the pilot positions is focused while estimating the channel. To neutralize the imaginary in terference, multiple auxiliary symbols have been proposed to enhance the throughput and channel capacity. The Iterative Minimum Mean Squared Error (IMMSE) cancellation scheme has been proposed to reduce the interference at the pilot and data positions. Transmission power, Bit Error Rate (BER) and throughput are computed for Filter Bank Multi Carrier (FBMC), OFDM and proven that better system performance is obtained for FBMC. The performance of channel estimation is evaluated through 5G standards and indicates that the usage of multiple auxiliary symbols per pilot leads to better throughput and low Bit Error Rate at low power transmission.

Deep Learning-Based SC-FDMA Channel Equalization

It is very challenging to design an effective wireless communication system. That’s because of numerous factors affecting the performance of a typical wireless communication system, such as nonlinear channel distortions and impairments. single carrier frequency division multiple access (SC-FDMA) is a multiple access scheme that is an important part of the long-term evolution (LTE) standard for uplink transmission. An advanced mobile radio system’s multiple access schemes should indeed meet stringent requirements, such as a low bit error rate (BER). In this article, we investigate the equalization problem for nonlinear channel distortions and impairments using deep neural networks (NN). We introduce a novel combined deep neural network channel equalization and symbol detection scheme based on a deep learning (DL) recurrent feedback (RF) long short-term memory (LSTM) neural network to achieve blind equalization and decoding for SC-FDMA systems without knowing the channel state information (CSI). To train the model efficiently, the training data is gathered by simulation, with channel effects and noise treated as a complete black box. CSI and constellation demapping are learned by a deep neural network (DNN) model. Then, the frequency-domain sequences that have been corrupted are implicitly equalized to get the broadcasted signal back. Our specified SC-FDMA system, which uses a quadrature phase-shift keying (QPSK) modulation method and the suggested Deep Learning-based model channel equalizer, performs better than the existing equalizers by an average of 1 to 4 dB at moderate signal-to-noise (SNR) ratios, according to simulation data. A complexity comparison between the proposed and the conventional equalizers was conducted in terms of training time, execution time, and number of operations. On combined channel equalization and symbol detection, the suggested system delivers state-of-the-art performance.

First-principles study of F adsorption by TiN with its oxide surface in three-dimensional NAND flash memory

Three-dimensional (3D) NAND flash memory is a key technology in the field of mainstream memory solutions, which is primarily due to its extremely low bit cost. The architecture of 3D NAND, characterized by its vertically stacked design, substantially enhances the capacity of individual chips. This advancement is completely consistent with the demands for high-capacity data storage in contemporary environments, securing its widespread adoption in diverse application scenarios. As storage density increases, the complexity of process integration increases, bringing new challenges. The word lines in 3D NAND are typically filled by using gate replacement techniques, and compared with chemical vapor deposition (CVD), atomic layer deposition (ALD) is favored for its superior step-coverage, especially for depositing tungsten (W) at the gate. However, due to the complexity of the replacement gate deposition structure, fluorine (F) residues are found in the voids of the tungsten metal gate filling structure and diffuse into the surrounding structure under subsequent process conditions, corroding other films such as silicon oxide and degrading device performance and reliability. To alleviate the problem of fluorine attack, a thin layer of titanium nitride is usually deposited as a barrier layer before deposition of tungsten gate, which blocks the fluorine in the tungsten gate and prevents its diffusion into the oxide layer. Previously, there were studies to increase the ability to stop F diffusion by varying the thickness of the F blocking layer (TiN). However, increasing the thickness of TiN will further increase the complexity of high aspect ratio etching in the 3D NAND process, which will have adverse effect on subsequent processes. To further minimize the effect of fluorine erosion, residual fluorine elements can be removed by introducing annealing in the subsequent process flow. In the actual 3D NAND process, elemental fluorine (F) is adsorbed and accumulates on the TiN surface, and is further activated by subsequent high-temperature processes, leading to severe fluorine erosion. The delay between TiN deposition and subsequent processing steps is hypothesized to facilitate fluorine adsorption due to the oxidation of TiN. This work corroborates this hypothesis through first-principles calculations, and demonstrates the role of TiN oxidation in fluorine adsorption. In this work, we evaluate the effect of this oxidation on the fluorine-blocking effectiveness of the TiN barrier layer. We simulate the adsorption of fluorine-containing by-products on TiN and its oxides, providing theoretical insights into mitigating fluorine attack. The higher degree of oxidation of TiN is more likely to cause F adsorption, and Ti exposed surface TiN is more prone to oxidation, which is more likely to cause F adsorption in unoxidized condition and oxidized condition. Based on these insights, we implemente an ammonia purge treatment in 3D NAND manufacturing, which effectively minimizes fluorine attack, reducing the leakage probability of word line by 25% and wafer warpage by 43%.

Analysis of Arithmetic Modular Code Applied to Data Transmission in the GLONASS Satellite Navigation System

The paper explores the effectiveness of modular coding for transmit- ting information message under low bit energy/noise ratio conditions. The analysis was conducted as part of the project aimed at enhancing the current GLONASS system and involved comparing it with the truncated Hamming code (85,77). During the analysis, we have calculated the minimum arithmetic operations needed to encode both codes and assessed the noise immunity of modular coding. When comparing the required arithmetic operations for coding, modular code only holds an advantage in cases where the numerical information is comparable to the bases of the system. Additionally, its noise immunity is inferior when transmitting the same amount of information bits as in the Hamming code. However, when reducing the bases of the system, modular code prevails. Additionally, it is worth noting that the system possesses certain features, such as the ability to process the coded information in parallel and flexible methods of decoding the information.

Multi-Agent Deep Reinforcement Learning for Spectral Efficiency Optimization in Vehicular Optical Camera Communications

In this paper, we propose a vehicular optical camera communication system that can meet low bit error rate (BER) and ultra-low latency constraints. First, we formulate a sum spectral efficiency optimization problem that aims at finding the speed of vehicles and the modulation order that maximizes the sum spectral efficiency subject to reliability and latency constraints. This problem is mixed-integer programming with nonlinear constraints, and even for a small set of modulation orders, is NP-hard. To overcome the entailed high computational and time complexity which prevents its solution with traditional methods, we first model the optimization problem as a partially observable Markov decision process. We then solve it using an independent Q-learning framework, where each vehicle acts as an independent agent. Since the state-action space is large we then adopt deep reinforcement learning (DRL) to solve it efficiently. As the problem is constrained, we employ the Lagrange relaxation approach prior to solving it using the DRL framework. Simulation results demonstrate that the proposed DRL-based optimization scheme can effectively learn how to maximize the sum spectral efficiency while satisfying the BER and ultra-low latency constraints. The evaluation further shows that our scheme can achieve superior performance compared to radio frequency-based vehicular communication systems and other vehicular OCC variants of our scheme.

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.