HDTV Research Articles

Video Generative AI in Digital Marketing: A SWOT Analysis on E-Commerce

Artificial Intelligence (AI) is reshaping the way various business processes are performed, most rapidly in digital marketing, due to its advanced computational capabilities. ECommerce companies are in advantageous position to adopt and reap the benefits of AI for many reasons including the readily available computational infrastructure and big data related to their users and processes. Meanwhile, big tech companies and startups are competing to create and improve generative AI models, that create high-definition videos based on inputs in the form of text, image or videos. The current research specifically aims to analyse the video outputs of various video generating AI models available for digital marketing purposes in ecommerce businesses and to rationally analyse how those video generative AI models benefit or affect e-commerce companies. To meet the research objectives, mixed methodologies of experimentation and content analysis were deployed. It was observed that the video outputs from the tested models were not yet suitable for digital marketing and appeared underdeveloped. Additionally, the SWOT analysis revealed advantages like low costs, alongside concerns such as intellectual property issues.

Cloud IaaS Optimization Using Machine Vision at the IoT Edge and the Grid Sensing Algorithm

Security grids consisting of High-Definition (HD) Internet of Things (IoT) cameras are gaining popularity for organizational perimeter surveillance and security monitoring. Transmitting HD video data to cloud infrastructure requires high bandwidth and more storage space than text, audio, and image data. It becomes more challenging for large-scale organizations with massive security grids to minimize cloud network bandwidth and storage costs. This paper presents an application of Machine Vision at the IoT Edge (Mez) technology in association with a novel Grid Sensing (GRS) algorithm to optimize cloud Infrastructure as a Service (IaaS) resource allocation, leading to cost minimization. Experimental results demonstrated a 31.29% reduction in bandwidth and a 22.43% reduction in storage requirements. The Mez technology offers a network latency feedback module with knobs for transforming video frames to adjust to the latency sensitivity. The association of the GRS algorithm introduces its compatibility in the IoT camera-driven security grid by automatically ranking the existing bandwidth requirements by different IoT nodes. As a result, the proposed system minimizes the entire grid’s throughput, contributing to significant cloud resource optimization.

Autonomous tracking of honey bee behaviors over long-term periods with cooperating robots.

Digital and mechatronic methods, paired with artificial intelligence and machine learning, are transformative technologies in behavioral science and biology. The central element of the most important pollinator species-honey bees-is the colony's queen. Because honey bee self-regulation is complex and studying queens in their natural colony context is difficult, the behavioral strategies of these organisms have not been widely studied. We created an autonomous robotic observation and behavioral analysis system aimed at continuous observation of the queen and her interactions with worker bees and comb cells, generating behavioral datasets of exceptional length and quality. Key behavioral metrics of the queen and her social embedding within the colony were gathered using our robotic system. Data were collected continuously for 24 hours a day over a period of 30 days, demonstrating our system's capability to extract key behavioral metrics at microscopic, mesoscopic, and macroscopic system levels. Additionally, interactions among the queen, worker bees, and brood were observed and quantified. Long-term continuous observations performed by the robot yielded large amounts of high-definition video data that are beyond the observation capabilities of humans or stationary cameras. Our robotic system can enable a deeper understanding of the innermost mechanisms of honey bees' swarm-intelligent self-regulation. Moreover, it offers the possibility to study other social insect colonies, biocoenoses, and ecosystems in an automated manner. Social insects are keystone species in all terrestrial ecosystems; thus, developing a better understanding of their behaviors will be invaluable for the protection and even the restoration of our fragile ecosystems globally.

An Improved Phase Noise Reduction in Millimeter-Wave FBMC Systems

The next generation mobile network needs to have a spectrum above the 30 GHz radio band. The 5G system is a promising technology for future communication systems. It has been used in Device-to-Device (D2D) communications, IoT, Machine type communications (MTC), and wireless backhaul. The 5G is expected to have speeds of hundreds of times and satisfy the requirements of larger traffic density, low latency, connective density, higher capacity & reliability, data rate, super high mobility, etc. It is expected to have applications such as HD video, virtual reality & augmented reality, online games, and cloud desktops. For this, Millimeter wave (Mmwave) has been used for fifth-generation mobile networks. The millimeter wave has a frequency range from 30 to 300 GHz. When the usage of frequency is above 60 GHz, the band will acquire RF impairments such as PAPR, Phase noise, non-linearity, and phase and quadrature timing mismatch (IQTM). This article shows the Phase noise reduction in MIMO FBMC systems using Extended Kalman filter techniques.

An open-access database of video stimuli for action observation research in neuroimaging settings: psychometric evaluation and motion characterization.

Video presentation has become ubiquitous in paradigms investigating the neural and behavioral responses to observed actions. In spite of the great interest in uncovering the processing of observed bodily movements and actions in neuroscience and cognitive science, at present, no standardized set of video stimuli for action observation research in neuroimaging settings exists. To facilitate future action observation research, we developed an open-access database of 135 high-definition videos of a male actor performing object-oriented actions. Actions from 3 categories: kinematically natural and goal-intact (Normal), kinematically unnatural and goal-intact (How), or kinematically natural and goal-violating (What), directed toward 15 different objects were filmed from 3 angles. Psychometric evaluation of the database revealed high video recognition accuracy (Mean accuracy = 88.61 %) and substantial inter-rater agreement (Fleiss' Kappa = 0.702), establishing excellent validity and reliability. Videos' exact timing of motion onset was identified using a custom motion detection frame-differencing procedure. Based on its outcome, the videos were edited to assure that motion begins at the second frame of each video. The videos' timing of category recognition was also identified using a novel behavioral up-down staircase procedure. The identified timings can be incorporated in future experimental designs to counteract jittered stimulus onsets, thus vastly improving the sensitivity of neuroimaging experiments. All videos, their psychometric evaluations, and the timing of their frame of category recognition, as well as our custom programs for performing these evaluations on our, or on other similar video databases, are available at the Open Science Framework (https://osf.io/zexc4/).

High-frequency study of megafaunal communities on whale bone, wood and carbonate in hypoxic Barkley Canyon

Organic-rich whale bones and wood falls occur widely in the deep sea and support diverse faunal communities, contributing to seafloor habitat diversity. Changes in community structure through succession on deep-sea bone/wood substrates are modulated by ecosystem engineers, i.e., bone-eating Osedax annelids, and wood-boring Xylophaga bivalves. Here, we use a comparative experimental approach and Ocean Networks Canada’s (ONC) cabled observatory in hypoxic Barkley Canyon to study, at high temporal resolution, colonization and succession on whale-bone, Douglas fir wood, and control carbonate rock over 8.3 mo. Experimental substrates were similar in size and mounted on PVC plates near the seafloor at 890 m depth and monitored by high-definition video camera for 5-min intervals every 6-12 h over a period of 8.3 mo. A broad range of seafloor and sea-surface environmental variables were also monitored at this site over the 8.3 mo to account for environmental variability and food input. Following loss of the high-definition camera, substrates were surveyed approximately annually with lower resolution ROV video for an additional 8.5 y. We find that megafaunal abundances, species diversity, and community structure varied substantially over 8.3 mo on each substrate, with markedly different patterns on whale bones due to the development of extensive white bacterial mats. A combination of seafloor and sea surface variables explained < 35% of bone/wood community variation. Bone-eating Osedax annelids failed to colonize whale bones even after 9.2 years, and boring Xylophaga bivalves colonized the wood at much lower rates than in better oxygenated deep-sea locations. Species diversity on whale-bone and wood substrates appeared to be substantially reduced due to the absence of ecosystem engineers and the low oxygen concentrations. We hypothesize that Osedax/Xylophaga colonization, bone/wood degradation, and bone/wood community development may be limited by oxygen concentrations of 0.22 - 0.33 ml.l on the NE Pacific margin, and that OMZ expansion due to climate change will reduce whale-bone and wood degradation, and the contribution of whale falls and wood falls to beta diversity, on the NE Pacific margin.

Application of Seagull Optimization Algorithm-BP Neural Network in Oil-Water Two-Phase Flow Pattern Forecasting

With the ongoing increase in global energy demand, the significance of innovations in oil exploration and development technologies is rising, especially in relation to the development of unconventional reservoirs. The application of horizontal wells is becoming increasingly important in this particular situation. However, accurately monitoring and analyzing fluids in horizontal wells remains challenging due to the complex and fluctuating flow patterns of oil-water two-phase flow within the wellbore. Several elements, including well slope angle, flow rate, and water content, are involved. This study aimed to explore and develop an effective method for forecasting flow patterns, improving the precision of the dynamic monitoring of oil-water two-phase flow in horizontal wells. By analyzing the flow patterns in different experimental conditions, a predictive model using the SOA-BP neural network was developed, providing a scientific basis for dynamic monitoring in actual production scenarios. Initially, the simulated experiment for oil-water two-phase flow was carried out at room temperature and pressure utilizing a multiphase flow simulator. An optically transparent wellbore, with a diameter comparable to that of a real downhole well, was utilized, and No. 10 industrial white oil and tap water were employed as the experimental fluids. The experiment considered multiple contributing factors, including different well deviation, total flow, and water cut. The flow characteristics of oil and water were observed via visual monitoring and high-definition video, followed by detailed analysis. After collecting the experimental data, flow regimes for various scenarios were classified based on the established theory of oil-water two-phase flow in horizontal wells; then, detailed flow distribution diagrams were drawn. These data and diagrams presented offer a visual representation of the behavioral patterns exhibited by oil-water two-phase flow under varying situations and form the basis for subsequent model training and testing. Subsequently, based on the experimental data, this study combined the Seagull Optimization Algorithm (SOA) with a BP neural network to effectively learn and predict the experimental data. The SOA optimized the weights and biases of the BP neural network, improving the model’s convergence speed and prediction accuracy. Through rigorous training and testing, an oil-water two-phase flow pattern forecasting model was established, effectively predicting flow patterns under different well deviation, total flow, and water cut conditions. Finally, to validate the efficiency of the established model, a total of 15 data points were chosen from a sample well for validation. By comparing the flow patterns predicted by the model with actual logging data, the results indicate that the model’s accuracy in identifying flow pattern was 86.67%. This demonstrates that the flow pattern prediction model based on the SOA-BP neural network achieved a high level of accuracy under different complicated working conditions. This model effectively fulfills the requirements for dynamic monitoring in actual production. This indicates that the SOA-BP neural network-based flow pattern forecasting method is highly valuable due to its practical application value and provides an efficient technical approach for the development of unconventional reservoirs and the dynamic monitoring of horizontal wells in the future.

Enhanced 2D Hand Pose Estimation for Gloved Medical Applications: A Preliminary Model.

(1) Background: As digital health technology evolves, the role of accurate medical-gloved hand tracking is becoming more important for the assessment and training of practitioners to reduce procedural errors in clinical settings. (2) Method: This study utilized computer vision for hand pose estimation to model skeletal hand movements during in situ aseptic drug compounding procedures. High-definition video cameras recorded hand movements while practitioners wore medical gloves of different colors. Hand poses were manually annotated, and machine learning models were developed and trained using the DeepLabCut interface via an 80/20 training/testing split. (3) Results: The developed model achieved an average root mean square error (RMSE) of 5.89 pixels across the training data set and 10.06 pixels across the test set. When excluding keypoints with a confidence value below 60%, the test set RMSE improved to 7.48 pixels, reflecting high accuracy in hand pose tracking. (4) Conclusions: The developed hand pose estimation model effectively tracks hand movements across both controlled and in situ drug compounding contexts, offering a first-of-its-kind medical glove hand tracking method. This model holds potential for enhancing clinical training and ensuring procedural safety, particularly in tasks requiring high precision such as drug compounding.

EF yolov8s: A Human–Computer Collaborative Sugarcane Disease Detection Model in Complex Environment

The precise identification of disease traits in the complex sugarcane planting environment not only effectively prevents the spread and outbreak of common diseases but also allows for the real-time monitoring of nutrient deficiency syndrome at the top of sugarcane, facilitating the supplementation of relevant nutrients to ensure sugarcane quality and yield. This paper proposes a human–machine collaborative sugarcane disease detection method in complex environments. Initially, data on five common sugarcane diseases—brown stripe, rust, ring spot, brown spot, and red rot—as well as two nutrient deficiency conditions—sulfur deficiency and phosphorus deficiency—were collected, totaling 11,364 images and 10 high-definition videos captured by a 4K drone. The data sets were augmented threefold using techniques such as flipping and gamma adjustment to construct a disease data set. Building upon the YOLOv8 framework, the EMA attention mechanism and Focal loss function were added to optimize the model, addressing the complex backgrounds and imbalanced positive and negative samples present in the sugarcane data set. Disease detection models EF-yolov8s, EF-yolov8m, EF-yolov8n, EF-yolov7, and EF-yolov5n were constructed and compared. Subsequently, five basic instance segmentation models of YOLOv8 were used for comparative analysis, validated using nutrient deficiency condition videos, and a human–machine integrated detection model for nutrient deficiency symptoms at the top of sugarcane was constructed. The experimental results demonstrate that our improved EF-yolov8s model outperforms other models, achieving mAP_0.5, precision, recall, and F1 scores of 89.70%, 88.70%, 86.00%, and 88.00%, respectively, highlighting the effectiveness of EF-yolov8s for sugarcane disease detection. Additionally, yolov8s-seg achieves an average precision of 80.30% with a smaller number of parameters, outperforming other models by 5.2%, 1.9%, 2.02%, and 0.92% in terms of mAP_0.5, respectively, effectively detecting nutrient deficiency symptoms and addressing the challenges of sugarcane growth monitoring and disease detection in complex environments using computer vision technology.

High-speed 0.22 THz communication system with 84 Gbps for real-time uncompressed 8K video transmission of live events

Terahertz frequency band (0.1 ~ 10 THz) can provide ultra-high transmitting rates for massive emerging applications. However, due to the radio frequency impairments and limited signal processing property of baseband devices, it is difficult for the current terahertz communication systems to be used in real applications. In this work, we report a 0.22 THz communication system, which can provide 84 Gbps air interface rate over 1.26 kilometers distance. Such a system is underpinned by three breakthroughs, namely, integrating the reported terahertz wireless communication system, redesigning the signal processing in baseband devices, and increasing the cut-off frequency and transmitting power of radio frequency component by parameter optimizing. Being applied to realize the real-time transmission of the uncompressed 8K ultra high-definition video in the 31st International University Sports Federation in Chengdu, China, this work is a leaping advancement to enable terahertz systems from experiment to promising applications.

The preservation of high-definition video data at the USC Digital Repository: The Television Academy Foundation’s Interviews Collection

This paper presents a comprehensive case study examining a large-scale video migration project carried out by the USC Digital Repository involving oral history interviews from the Television Academy Foundation, captured in high-definition video (HDV). Unlike the more-widely compatible MiniDV format, HDV poses unique challenges due to its distinct format standard, rendering many open source toolsets geared towards capturing DV content incompatible. Consequently, this leaves archivists to rely on unsupported, proprietary tools, exacerbating the complexities inherent in large-scale migrations. Before presenting the troubleshooting process, this paper provides an overview of The Foundation’s Interviews collection, which includes over 900 interviews spanning over 3,000 hours of catalogued and publicly accessible first-hand accounts of television industry pioneers and professionals. Subsequently, the paper delves into the technical intricacies of the HDV format and the challenges of migrating data from HDV. It examines the strategies considered for identifying and addressing corrupt data, alongside implemented solutions aimed at balancing data authenticity with usability in the face of inevitable corruption occurrences throughout the collection. This study not only sheds light on the challenges inherent in preserving HDV content but also offers guidance for future endeavours seeking to safeguard valuable cultural and historical materials in similar formats.

Effect of flameholding cavity geometry on the flowfield of a solid fuel scramjet

An optically accessible solid fuel scramjet was used to investigate the supersonic combustion of varying polymethyl methacrylate solid fuel grain geometries. The rear wall angle of the flameholding cavity was varied (30, 60, 75, and 90°) and an alternative geometry (flush fuel grain) containing the addition of a backwards facing step to the front of a conventional fuel grain was explored. Optical diagnostics including high-speed shadowgraph, CH* chemiluminescence, and HD video were used to analyze flammability characteristics and determine quantitative regression rates for each geometry. The optical results were supplemented by static pressure traces along the length of the combustor for the duration of a test firing. All tests were conducted with Mach 2 inlet flow into the combustor at nominal inlet conditions of 12.9 atm and 900 °C. Shadowgraph imaging for both cold flow and combusting flow was used to characterize the varying flow fields associated with their respective geometries and their core flow oscillatory frequencies were preliminarily inspected. Chemiluminescence data reveals increased heat release in the downstream constant area and expansion sections of the flush fuel grains compared to conventional geometries. This heat release appears to correlate with increased time-averaged regression rates in these regions while the varying cavity angle fuel grains demonstrate differing magnitudes of maximum regression along the rear cavity wall and onset of the constant area section. In addition to increased overall regression rates, flush geometries increase the flameholding capabilities and demonstrate self-sustained burning at lower inlet temperatures than similar geometries designed without a flush section.

Toward a revision of the bamboo corals: Part 6, Illuminating a new candelabrum genus (Octocorallia: Keratoisididae)

Observations and collections made using remotely operated vehicles (ROV) outfit with high-definition video cameras on bathyal seamounts of the North Atlantic and North Pacific have revealed a bamboo coral (Octocorallia, Keratoisididae) with consistent and recognizable colony morphology: a long unbranched “stem” from which many vertically aligned branches arise in a single plane to produce the aspect of a tall candelabrum. Additional observations encountered colonies with only 3 branches arising from the central terminal node to produce the appearance of a standing trident. Genetic analyses suggested both colony morphologies (trident and candelabrum) to be the same species at different growth stages. Herein we formally describe this taxon, Tridentisis candelabrum n. gen. n. sp., erecting a new genus to accommodate the unique and distinctive colony morphology, and discuss morphological variation observed across the documented geographic range.

On-chip topological beamformer for multi-link terahertz 6G to XG wireless.

Terahertz (THz) wireless communication holds immense potential to revolutionize future 6G to XG networks with high capacity, low latency and extensive connectivity. Efficient THz beamformers are essential for energy-efficient connections, compensating path loss, optimizing resource usage and enhancing spectral efficiency. However, current beamformers face several challenges, including notable loss, limited bandwidth, constrained spatial coverage and poor integration with on-chip THz circuits. Here we present an on-chip broadband THz topological beamformer using valley vortices for waveguiding, splitting and perfect isolation in waveguide phased arrays, featuring 184 densely packed valley-locked waveguides, 54 power splitters and 136 sharp bends. Leveraging neural-network-assisted inverse design, the beamformer achieves complete 360° azimuthal beamforming with gains of up to 20 dBi, radiating THz signals into free space with customizable user-defined beams. Photoexciting the all-silicon beamformer enables reconfigurable control of THz beams. The low-loss and broadband beamformer enables a 72-Gbps chip-to-chip wireless link over 300 mm and eight simultaneous 40-Gbps wireless links. Using four of these links, we demonstrate point-to-4-point real-time HD video streaming. Our work provides a complementary metal-oxide-semiconductor-compatible THz topological photonic integrated circuit for efficient large-scale beamforming, enabling massive single-input multiple-output and multiple-input and multiple-output systems for terabit-per-second 6G to XG wireless communications.

DRTP: A generic Differentiated Reliable Transport Protocol

With rapid development of network communications, the performance requirements of applications are getting more differentiated. Many applications like high-definition video transfer require high throughput but do tolerate occasional packet losses. Traditional generic transport protocols however, only provide inflexible data transfer guarantees (TCP (Transmission Control Protocol) offers full reliability guarantees and UDP (User Datagram Protocol) offers no guarantees). Moreover, TCP pays a significant price to ensure a full reliability guarantee over lossy wireless communications environment like 5G millimeter wave (mmWave) communications. While existing, “partially” reliable transport protocols are either specifically designed for certain applications or need router’s support. In this paper, we design a new generic Differentiated Reliable Transport Protocol (DRTP), aiming to provide a differentiated and deterministic reliability guarantee for upper layer applications while maximizing the throughput under the constraint of guaranteeing a required reliability of data transfer. DRTP is a generic and pure end-to-end partially reliable transport protocol, and as such is easy to deploy regardless of the application in use and with no need for router’s support. The performance of DRTP is evaluated under various network conditions using extensive NS-3 (Network Simulator) simulations and practical experiments over the mmWave communications environment. The results show much higher throughput compared to typical transport protocols while guaranteeing the required transfer reliability.

Situating love and loss: making a film, re-making a world

ABSTRACT The title of the author’s film ‘HOW PERFECT IS THIS HOW BLESSED ARE WE’ (HD video, 22 min and 11 s) is taken from the inscription on a bench on the top of a hill, at the highest point in the landscape of Compton Verney, designed by Lancelot ‘Capability’ Brown in his ideal vision of the British country house. By appropriating this statement of contentment as a critical tool to inquire how perfect it really is and who is the ‘we’ that is so blessed, Suleman inserted their body into the landscape as a reminder of historical absences and erasure, and the legacy of colonialism and queer-phobia. The film is narrated, however, in the register of an address to an absent lover. A personal history and a lover’s yearning doubles as a critique of violent and exclusive histories.

The Technology Evolution and Future Trend of Smart Blackboard

As an important tool for educational digitization, the function of the smart blackboard has gradually evolved from a single teaching information-sharing tool to a smart system for knowledge expression, sharing and generation. Smart blackboard technology has been experienced from chalk + blackboard to projection equipment + blackboard, to interactive electronic whiteboard, and then to today's integrated touch, high-definition display and artificial intelligence technology. The evolution process of the smart blackboard is characterized by interactivity, effectiveness and intelligence. At present, the smart blackboard has become a collection of many advanced technologies. It is gradually integrated into modern technologies such as artificial intelligence, cloud computing, big data analysis and the Internet of Things, making remote collaboration and intelligent classroom management possible. It has become a smart system for big data collection, mining and analysis, evaluation and feedback, and teaching decision-making. Looking forward to the future, the smart blackboard will pay more attention to user experience design, provide an immersive learning experience through Augmented Reality and Virtual Reality technology, and optimize personalized teaching content in combination with machine learning, to lay a foundation for building an intelligent and personalized teaching environment.

Microscale Lateral Perovskite Light Emitting Diode Realized by Self-Doping Phenomenon.

High-definition near-eye display technology has extremely close sight distance, placing a higher demand on the size, performance, and array of light-emitting pixel devices. Based on the excellent photoelectric performance of metal halide perovskite materials, perovskite light-emitting diodes (PeLEDs) have high photoelectric conversion efficiency, adjustable emission spectra, and excellent charge transfer characteristics, demonstrating great prospects as next-generation light sources. Despite their potential, the solubility of perovskite in photoresist presents a hurdle for conventional micro/nano processing techniques, resulting in device sizes typically exceeding 50 μm. This limitation impedes the further downsizing of perovskite-based components. Herein, we propose a plane-structured PeLED device that can achieve microscale light-emitting diodes with a single pixel device size < 2 μm and a luminescence lifetime of approximately 3 s. This is accomplished by fabricating a patterned substrate and regulating ion distribution in the perovskite through self-doping effects to form a PN junction. This breakthrough overcomes the technical challenge of perovskite-photoresist incompatibility, which has hindered the development of perovskite materials in micro/nano optoelectronic devices. The strides made in this study open up promising avenues for the advancement of PeLEDs within the realm of micro/nano optoelectronic devices.

RegionFilter: Region-aware video filtering mechanism on resource-constrained edge nodes

The video analytics pipeline consumes substantial network and computing resources, and in many scenarios videos are sent from cameras to servers. Due to limited camera-side resources, most existing systems are frame-level filtering approaches that filter out frames that are unchanged or irrelevant to applications before transmitting data. However, video applications usually care about the regions of interest (RoIs) of frames rather than background regions, and frame-level filtering fails to consider the spatial redundancy of intra-frame regions. We argue that video analytics should adopt a region-level filtering approach. Unfortunately, how to perform region-aware filtering on cameras without GPU is a significant challenge. To overcome this, we built RegionFilter, which achieves region awareness on resource-constrained edge nodes through feedback results from server-side DNN models. RegionFilter splits each video segment into multiple sub-segments in spatial distribution through region awareness. Then different configuration parameters (e.g., resolution, quantization parameter (QP)) are selected according to the video application, and the sub-segments are encoded into sub-streamings with different qualities that are uploaded to servers for further processing. Experiments on various video tasks and datasets show that RegionFilter achieves considerable filtering benefits (bandwidth savings of 11.7–41.2% when transmitting HD video) while always meeting the desired accuracy.

A Comprehensive Comparison of Simplified Volterra Equalization and Kramers–Kronig Schemes in 200 Gb/s/λ PON Downlink Transmission

The emerging high-bandwidth services of 6G, such as high-definition video transmission and real-time interaction, have promoted the progress of the fiber optic access network industry, driving its development towards the next-generation PON with higher speed and larger system capacity. In response to the future requirements of 200 Gb/s/λ PON for both 20 km and 30 km downlink transmission scenarios, this paper proposes a Simplified Volterra Equalization (SVLE) scheme based on Nyquist PAM4 single-sideband modulation direct detection (SSBM-DD) scheme. In order to verify its advantages, the IQ-modulated Kramers–Kronig reception (KK) scheme is introduced for comparison. Simulation validation platforms for two schemes are conducted, and the performance comparison of the SVLE and KK schemes is carried out. In both, the impact of the carrier signal power ratio (CSPR) on receiver sensitivity, the influence of input optical power on power budget and receiver sensitivity, and the tolerance of receiver sensitivity to the linewidth of the DFB laser are investigated in the simulation. Finally, a comprehensive comparison of the two schemes is presented in terms of system performance, cost, and DSP complexity. In the 20 km downlink transmission scenario, the SVLE scheme outperforms the KK scheme by 4.9 dB in terms of power budget. The total number of multiplications of the SVLE scheme is 37, while that of the KK scheme is 4358. Therefore, the DSP complexity of the SVLE scheme is much lower than that of the KK scheme. The results of the comparison demonstrate that, in downlink transmission scenarios, the SVLE scheme is more suitable than the KK scheme as it exhibits a higher power budget, lower DSP complexity, and lower cost. Consequently, the proposed SVLE scheme could be a highly promising solution for future ultra-high-speed PON downlink transmission.