Full HD Research Articles

Performance Comparison of VVC, AV1, HEVC, and AVC for High Resolutions

Over the years, there has been growing interest in multimedia services, especially in the video domain, where firms and subscribers require higher resolutions, framerates, and sampling precision. This results in a huge amount of data that needs to be processed, stored, and transmitted. As a result, researchers face the challenge of developing new compression standards that can reduce the amount of data while maintaining the same quality. In this paper, the compression performance of the latest and most commonly used video codecs, namely H.266/VVC, AV1, H265/HEVC, and H.264/AVC was examined. The test set included seven sequences of various content at 8K, Ultra HD (UHD), and Full HD (FHD) resolutions, encoded to bitrates ranging from 1 to 15 Mbps for FHD and UHD resolutions and from 5 to 50 Mbps for 8K resolution. Objective quality metrics, such as peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM), and video multi-method assessment fusion (VMAF) were used to measure codec performance. The results showed that H.266/VVC outperformed all other codecs, namely H.264/AVC, H.265/HEVC, and AV1, in terms of the Bjøntegaard delta (BD) model. The average bitrate savings were approximately 78% for H.266/VVC, 63% for AV1, and 53% for H.265/HEVC relative to H.264/AVC, 59% for H.266/VVC and 22% for AV1 compared to H.264/AVC, and 46% for H.266/VVC relative to AV1 (all for 8K resolution). The results also showed that codec performance varied depending on resolution, with higher resolutions showing greater efficiency for newly developed codecs, such as H.266/VVC and AV1. This confirms the fact that the H.266/VVC and AV1 codecs were primarily developed for videos at high resolutions, such as 8K and/or UHD.

A directionally illuminated pixel-selective flickering-free autostereoscopic display

A directionally illuminated pixel-selective flickering-free autostereoscopic display is proposed and demonstrated. The system consists of the U-shaped backlight, the mix-grooves cylindrical Fresnel lens array, a light shaping diffuser film, and a liquid crystal display with a directional light splitting element. Simulation is applied to obtain the crosstalk and the illuminance distribution at each viewpoint, which serves as design guideline for system structure and component parameters. The simulation and experimental results indicate that directional projection of parallax images is achieved for a high-quality flickering-free autostereoscopic display with a large viewing angle and continuous viewing volume. The crosstalk between left- and right-channels can be controlled down to 5.49%, while the resolution for each eye is more than a full HD. Furthermore, the alternative flickering in high refresh rate directional backlight autostereoscopic displays is removed by the proposed system, resulting in a high-quality and flickering-free viewing experience for multiple users.

Performance analysis of optimized versatile video coding software decoders on embedded platforms

In recent years, the global demand for high-resolution videos and the emergence of new multimedia applications have created the need for a new video coding standard. Therefore, in July 2020, the versatile video coding (VVC) standard was released, providing up to 50% bit-rate savings for the same video quality compared to its predecessor high-efficiency video coding (HEVC). However, these bit-rate savings come at the cost of high computational complexity, particularly for live applications and on resource-constrained embedded devices. This paper evaluates two optimized VVC software decoders, named OpenVVC and Versatile Video deCoder (VVdeC), designed for low resources platforms. These decoders exploit optimization techniques such as data-level parallelism using single instruction multiple data (SIMD) instructions and functional-level parallelism using frame, tile, and slice-based parallelisms. Furthermore, a comparison of decoding runtime, energy, and memory consumption between the two decoders is presented while targeting two different resource-constraint embedded devices. The results showed that both decoders achieve real-time decoding of full high-definition (FHD) resolution on the first platform using 8 cores and high-definition (HD) real-time decoding for the second platform using only 4 cores with comparable results in terms of the average energy consumed: around 26 J and 15 J for the 8 cores and 4 cores platforms, respectively. Furthermore, OpenVVC showed better results regarding memory usage with a lower average maximum memory consumed during runtime than VVdeC.

High-speed, low-voltage, small-pitch and robust OTFT-based integrated gate driver for active-matrix displays

We design and fabricate a high-speed, low-voltage, small-pitch, and robust integrated gate driver with organic thin-film transistors (OTFTs). The dual-gate OTFT devices have a field effect mobility of 0.78 cm2/Vs, and on/off current ratio above 108. A single stage of the gate driver consists of ten TFTs and two capacitors, with small pitch of 244 μm. The fabricated circuit has a minimum pulse width of 5μs, which is compatible with full-high definition (FHD, 1920 × 1080) active-matrix displays with 60Hz frame rate. It can work at input swing of as low as 10V, with VGH2, VGH1, and VGL of 5V, 0V, and −10V respectively. With the threshold voltage shift under positive and negative bias stress, the gate driver shows output signals without significant degradation and demonstrates its high reliability through simulation.

Potential of low-density genotype imputation for cost-efficient genomic selection for resistance to Flavobacterium columnare in rainbow trout (Oncorhynchus mykiss)

BackgroundFlavobacterium columnare is the pathogen agent of columnaris disease, a major emerging disease that affects rainbow trout aquaculture. Selective breeding using genomic selection has potential to achieve cumulative improvement of the host resistance. However, genomic selection is expensive partly because of the cost of genotyping large numbers of animals using high-density single nucleotide polymorphism (SNP) arrays. The objective of this study was to assess the efficiency of genomic selection for resistance to F. columnare using in silico low-density (LD) panels combined with imputation. After a natural outbreak of columnaris disease, 2874 challenged fish and 469 fish from the parental generation (n = 81 parents) were genotyped with 27,907 SNPs. The efficiency of genomic prediction using LD panels was assessed for 10 panels of different densities, which were created in silico using two sampling methods, random and equally spaced. All LD panels were also imputed to the full 28K HD panel using the parental generation as the reference population, and genomic predictions were re-evaluated. The potential of prioritizing SNPs that are associated with resistance to F. columnare was also tested for the six lower-density panels.ResultsThe accuracies of both imputation and genomic predictions were similar with random and equally-spaced sampling of SNPs. Using LD panels of at least 3000 SNPs or lower-density panels (as low as 300 SNPs) combined with imputation resulted in accuracies that were comparable to those of the 28K HD panel and were 11% higher than the pedigree-based predictions.ConclusionsCompared to using the commercial HD panel, LD panels combined with imputation may provide a more affordable approach to genomic prediction of breeding values, which supports a more widespread adoption of genomic selection in aquaculture breeding programmes.

Interactive Control over Temporal Consistency while Stylizing Video Streams

AbstractImage stylization has seen significant advancement and widespread interest over the years, leading to the development of a multitude of techniques. Extending these stylization techniques, such as Neural Style Transfer (NST), to videos is often achieved by applying them on a per‐frame basis. However, per‐frame stylization usually lacks temporal consistency, expressed by undesirable flickering artifacts. Most of the existing approaches for enforcing temporal consistency suffer from one or more of the following drawbacks: They (1) are only suitable for a limited range of techniques, (2) do not support online processing as they require the complete video as input, (3) cannot provide consistency for the task of stylization, or (4) do not provide interactive consistency control. Domain‐agnostic techniques for temporal consistency aim to eradicate flickering completely but typically disregard aesthetic aspects. For stylization tasks, however, consistency control is an essential requirement as a certain amount of flickering adds to the artistic look and feel. Moreover, making this control interactive is paramount from a usability perspective. To achieve the above requirements, we propose an approach that stylizes video streams in real‐time at full HD resolutions while providing interactive consistency control. We develop a lite optical‐flow network that operates at 80 Frames per second (FPS) on desktop systems with sufficient accuracy. Further, we employ an adaptive combination of local and global consistency features and enable interactive selection between them. Objective and subjective evaluations demonstrate that our method is superior to state‐of‐the‐art video consistency approaches. maxreimann.github.io/stream‐consistency

P17: “Vadoscopy” Of Universal Ventricular Assist Device Shut-Off Feature During Pump Operation

Objective: The automatic, universal ventricular assist device has been designed for biventricular support (RVAD or LVAD). Device architecture (UVAD, Fig. 1A, B) enables a life-saving automatic forward flow shut-off function that, in the event of power interruption, helps mitigate patient hazard. Herein, we report the endoscopic visualization (“vadoscopy”) approach used to directly observe the UVAD shut-off function during device activation. Methods: The UVAD implanted in an ex vivo model and connected to a mock circulatory loop was used to simulate pump performance conditions. Device insertions in RVAD and LVAD positions were individually tested. The endoscopic visualization (Karl Storz 5 mm, 30° endoscope; Olympus CH-S190-XZ-E/Q Full HD 3CCD Camera) of the pump rotor occurred during operation and shut-off. The endoscope was placed through left and right atrial appendage and secured with a purse-string, respectively for UVAD used for left (LVAD) and right ventricular (RVAD) support. Results: The circulatory mock loop and ex vivo heart circulation were successfully maintained for both RVAD and LVAD settings. Endoscope was introduced through hermetically sealed insertion point. Direct visualization of the moving UVAD rotor was achieved at various modulated speeds and flows (continuous and pulsatile). The visual confirmation of sub-optimal and full rotor shut-off function (complete closure) was obtained in RVAD and LVAD conditions (Fig. 1C, D, E, F). Conclusion: The UVAD aperture mechanism enabled optimal forward flow shut-off in continuous and pulsatile speed and flow modulation. Direct endoscopic visualization of the UVAD with moving rotor has been feasible. Additional device feature assessment is ongoing.Figure 1. A - UVAD with open aperture; B - UVAD closed aperture; C - Rotor view via inflow; D - Rotor at nominal speed; E - Simulated sub-optimal rotor closure with large gap and regurgitant flow; F - Full rotor shut-off (nearly zero flow) through aperture area.

(167) 3D Penile Reconstruction Imaging in Complex Peyronie’s Disease (PD): Proof of Concept Study

Abstract Introduction The evaluation of penile deformity in PD is a critical step in therapeutic decision making and is generally the main outcome in PD clinical trials. 3D reconstruction is becoming increasingly common in the medical field, with highly accurate and long term accessible images. To date, there is no widely accepted 3D documentation protocol for penile deformity. Objective To evaluate the feasibility of 3D imaging and its accuracy in quantifying penile deformity. Methods Video images captured intraoperatively during saline-induced rigid erection were acquired from 4 PD patients undergoing penile surgery. Penile curvature measurements were obtained by the surgeon with a goniometer. Video images were obtained with an iPhone 11 (Apple Inc, Cupertino, CA) camera in full HD or 4k resolution, traveling in a circular 360o motion around the penile shaft. Video images were analysed by a team with 3D expertise using the open source software Blender (Blender Foundation, Amsterdan, NE) and its addon OrtogonBlender, and 3D reconstructed images were created. The 3D team performed curvature measurements using the software tools. A non-concordance magnitude was defined as a measurement difference (between physician measurement and 3D image) of 20% or 10o, whichever was smaller. Results The 3D reconstruction was possible in all cases and all videos used took <1 minute to be captured. Images of 4 patients, 3 with bi-planar curvature, were obtained and therefore 7 measurements were available for caparison. The 3D images quality was good and volume loss deformities were visible. Measurements generated from those reconstructed images showed excellent concordance (100%, N=7 - Table 1). An example of curvature measurement in a 3D model is presented in Figure 1. Conclusions In this pilot study, deformity analysis and measurement with tolerable error was performed using video images acquired quickly intraoperatively, with a smartphone. With further development, this strategy may contribute to a more objective follow up of complex PD cases, as those with biplanar curvature or volume loss deformities. Disclosure No

High-Throughput Hardware Design for the AV1 Decoder Switchable Loop Restoration Filters

This paper presents a high-throughput hardware design for the Switchable Loop Restoration Filter (SLRF) of the AOM Video 1 (AV1) video format. This hardware includes the two filters defined at the AV1 SLRF: the Separable Symmetric Normalized Wiener Filter (SSNWF) and the Dual Self- Guided Filter (DSGF). The SLRF is the last step in the AV1 loop restoration filters, and it is used to attenuate blurring artifacts, improving the subjective video quality and the coding efficiency. The designed hardware targeted the AV1 decoderand is able to process up to 4K Ultra-High Definition (4K UHD) videos (with 3840x2160 pixels) at 60 frames per second (fps) in real-time. In order to cover different scenarios, two other target throughputs were also evaluated: 4K UHD at 30fps and Full HD (FHD) (with 1920x1080 pixels) at 30fps. The architectures were synthesized for standard cells using the 40 nm TSMC library. The SSNWF and DSGF architectures used 37.38 Kgates and 177.58 kgates in all evaluated scenarios. Depending on the evaluated scenario, the SSNWF power dissipation varied from 8.25mWto 26.95mWand the DSGF power varied from 57.19 mW to 115.02 mW. This is the first paper in the literature presenting a hardware design for the AV1 SLRF with its two filters.

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Hardware Architecture for Realtime HEVC Intra Prediction

Researchers have, in recent times, achieved excellent compression efficiency by implementing a more complicated compression algorithm due to the rapid development of video compression. As a result, the next model of video compression, High-Efficiency Video Coding (HEVC), provides high-quality video output while requiring less bandwidth. However, implementing the intra-prediction technique in HEVC requires significant processing complexity. This research provides a completely pipelined hardware architecture solution capable of real-time compression to minimize computing complexity. All prediction unit sizes of 4×4, 8×8, 16×16, and 32×32, and all planar, angular, and DC modes are supported by the proposed solution. The synthesis results mapped to Xilinx Virtex 7 reveal that our solution can do real-time output with 210 frames per second (FPS) at 1920×1080 resolution, called Full High Definition (FHD), or 52 FPS at 3840×2160 resolution, called 4K, while operating at 232 Mhz maximum frequency.

PD34-07 CAN CONVOLUTIONAL NEURAL NETWORK BE APPLIED TO THE PREDICTION OF URINARY STONE COMPOSITION IN SINGLE-USE FLEXIBLE URETEROSCOPIC IMAGES?

You have accessJournal of UrologyCME1 Apr 2023PD34-07 CAN CONVOLUTIONAL NEURAL NETWORK BE APPLIED TO THE PREDICTION OF URINARY STONE COMPOSITION IN SINGLE-USE FLEXIBLE URETEROSCOPIC IMAGES? Kyung Tak Oh, Dae Young Jun, Jae Young Choi, Dae Chul Jung, and Joo Yong Lee Kyung Tak OhKyung Tak Oh More articles by this author , Dae Young JunDae Young Jun More articles by this author , Jae Young ChoiJae Young Choi More articles by this author , Dae Chul JungDae Chul Jung More articles by this author , and Joo Yong LeeJoo Yong Lee More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003327.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: This study investigated whether Convolutional neural network (CNN) can show decent results in predicting urinary stone composition even in single-use flexible ureterorenoscopic (fURS) images with relatively low resolution. METHODS: This study retrospectively used surgical videos of fURS performed by a single surgeon using single-use scope (LithoVue) between 2018 and 2021. Digital images of the surface of the stone before laser fragmentation were captured in the surgical full HD video clips. Cases were divided into two groups according to whether they contained any calcium oxalate (Calcium group) or not (Non-calcium group). Among total 512 cases, 207 of stone surface images were finally included. Among total images, 25 were first designated as the test set, then augmentation for Non-calcium group was performed. The train set and validation set were divided by random split in an 8 to 2 ratio. In the CNN model, Resnet-18 model was used, and only endoscopic digital images and stone classification data were input to achieve minimal supervised learning (Fig 1). RESULTS: There were 175 cases in the Calcium group, and 32 cases in the Non-calcium group. After whole training, in validation set, the total accuracy was 82.0%, and recall, specificity and precision for Calcium group was 80.0%, 83.3% and 76.2%, and for Non-calcium group, it was 83.3%, 80.0% and 86.2%, respectively. Area under ROC curve of the model, which represents the classification performance of the model, was 0.863. After training and validation, the model was tested using the test set, and the total accuracy was 84.0%. Recall, specificity and precision of the test result was 80.0%, 100.0% and 100.0% in Calcium group, and 100.0%, 80.0% and 55.6% in Non-calcium group, respectively (Fig 2). CONCLUSIONS: As far as we know, this study is the first artificial intelligence study using a single-use fURS images. Source of Funding: None. © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e922 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Kyung Tak Oh More articles by this author Dae Young Jun More articles by this author Jae Young Choi More articles by this author Dae Chul Jung More articles by this author Joo Yong Lee More articles by this author Expand All Advertisement PDF downloadLoading ...

Impact of Packet Loss Rate on Quality of Compressed High Resolution Videos.

Video delivered over IP networks in real-time applications, which utilize RTP protocol over unreliable UDP such as videotelephony or live-streaming, is often prone to degradation caused by multiple sources. The most significant is the combined effect of video compression and its transmission over the communication channel. This paper analyzes the adverse impact of packet loss on video quality encoded with various combinations of compression parameters and resolutions. For the purposes of the research, a dataset containing 11,200 full HD and ultra HD video sequences encoded to H.264 and H.265 formats at five bit rates was compiled with a simulated packet loss rate (PLR) ranging from 0 to 1%. Objective assessment was conducted by using peak signal to noise ratio (PSNR) and Structural Similarity Index (SSIM) metrics, whereas the well-known absolute category rating (ACR) was used for subjective evaluation. Analysis of the results confirmed the presumption that video quality decreases along with the rise of packet loss rate, regardless of compression parameters. The experiments further led to a finding that the quality of sequences affected by PLR declines with increasing bit rate. Additionally, the paper includes recommendations of compression parameters for use under various network conditions.

An Efficient Deep-Learning-Based Super-Resolution Accelerating SoC With Heterogeneous Accelerating and Hierarchical Cache

This article presents an energy-efficient accelerating system-on-chip (SoC) for super-resolution (SR) image reconstruction on a mobile platform. With the rise of contactless communication and streaming services, the need for SR is growing. As one of the most basic low-level image processing algorithms, SR can reconstruct high-quality images from low-quality images which are noisy, compressed, or with damaged pixels. However, a massive amount of computation and considerable precision of pixel data pose challenges for acceleration in a resource and bandwidth constrained platform. SR has high energy consumption and long latency. While previous neural processing units (NPUs) reduced the precision to increase the efficiency and accelerate convolutional neural network (CNN) computation, few of them concentrated on both the output image quality and the performance of the entire system. The proposed SR SoC restores the high-quality image using a precision-optimized SR algorithm on an energy-efficient accelerating architecture and cache subsystem. It contributes three algorithm-hardware co-optimized features: 1) heterogeneous accelerating architecture (HAA) with only 8-bit floating-point (FP)-and-fixed-point (FXP) hybrid-precision for SR task; 2) tile-based hierarchical cache (THC) subsystem for the low energy and small footprint cost layer fusion; and 3) heterogeneous L1 data lifetime-aware optimized cache (DLOC) for the energy-efficient on-chip memory access. The prototype of SR SoC is fabricated in 65-nm technology and occupies a 10.0-mm2 die area. The proposed SR SoC can maintain the high reconstruction quality while consuming only 19% of the energy of an FXP16 system with homogeneous NPU. As a result, the SR SoC presents <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.6\times $ </tex-math></inline-formula> higher energy efficiency than the previous SR targeting NPU and achieves 107-frame-per-second (fps) framerates running <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> SR image generation to full high definition (FHD) scale at only 0.92-mJ/frame energy consumption.

Real-Time Multi-Object Detection Using Enhanced Yolov5-7S on Multi-GPU for High-Resolution Video

Multiple objects tracking in a video sequence can be performed by detecting and distinguishing the objects that appear in the sequence. In the context of computer vision, the robust multi-object tracking problem is a difficult problem to solve. Visual tracking of multiple objects is a vital part of an autonomous driving vehicle’s vision technology. Wide-area video surveillance is increasingly using advanced imaging devices with increased megapixel resolution and increased frame rates. As a result, there is a huge increase in demand for high-performance computation system of video surveillance systems for real-time processing of high-resolution videos. As a result, in this paper, we used a single stage framework to solve the MOT problem. We proposed a novel architecture in this paper that allows for the efficient use of one and multiple GPUs are used to process Full High Definition video in real time. For high-resolution video and images, the suggested approach is real-time multi-object detection based on Enhanced Yolov5-7S on Multi-GPU Vertex. We added one more layer at the top in backbone to increase the resolution of feature extracted image to detect small object and increase the accuracy of model. In terms of speed and accuracy, our proposed approach outperforms the state-of-the-art techniques.

Transmission of 3D holographic information over a radio channel by a method close to SSB

The results of the research on the possibility of transmitting holographic information over the Wi-Fi 40 MHz radio channel are presented. It is shown that the use of two main 3D image modalities for this, — a depth map of the holographic object and the texture of its surface, is sufficient to synthesize a full-fledged hologram at the receiving end of the communication channel, restoring the holographic object with continuous vertical and horizontal parallax. The method of transmitting 3D holographic information is similar to the well–known in radio engineering method of transmitting information on one sideband (Single-sideband modulation, SSB). The essential difference of the proposed method is that the spatial frequencies forming the hologram are the result of simultaneous amplitude and phase modulation of the reference signal. This complicates their theoretical analysis. Experimental confirmation of the possibility of such a transfer was performed using a free FTP client with open source FileZilla. A communication protocol has been applied to transmit information over a wireless Wi-Fi channel. It is shown that the transmitted information stream is sufficient to synthesize a hologram reconstructing 3D images at the receiving end of the communication channel. At the same time, the holographic image of a dynamically changing object with a television frame rate has continuous horizontal and vertical parallax, and the spatial resolution of the restored image was no worse than a high-definition television image of Full HD. The possibility of transmitting all the necessary information over the radio channel to reproduce a holographic 3D video stream at the receiving end of the channel with a resolution not lower than in high-definition television standards with continuous parallax has been experimentally confirmed.

Small-Scale Demonstration of Remote Control of Patrol and Work Robot with Arms Employing Local 5G System

Local (private) 5G system can provide a secure and flexible network using the cellular-based technologies at their facilities (e.g., factories, agricultural lands, and buildings). We constructed a small-scale demonstration system that exhibits the remote control of a patrol and work robot with arms using a local 5G system. The constructed robot comprises a robot operating system-based unmanned ground vehicle, two laser range finders, a webcam, an omnidirectional camera, and a six-axis robot arm. To fabricate a demonstration system with open-source software, we assessed the one-way delay of video streaming by changing different CPU, camera types, drivers, applications, and video resolutions. According to the assessment findings, it was demonstrated that it is possible to realize approximately 100ms delay under the limited resolution condition, and the allowable maximum absolute delay of 300ms can be attained even for full HD (1920 × 1080) resolution of this demonstration. Furthermore, local 5G was demonstrated to reduce delay variations to the same level as wired systems. It was also clarified that the increase in delay due to the application of local 5G is relatively small (5-25% in total delay) in this demonstration. Finally, we employed the small-scale demonstration system for the online and onsite campus tours for high school students.

Object Classification and Tracking Using Scaled P8 YOLOv4 Lite Model

One of the most difficult tasks in the area of computer vision is object detection, which combines object categorization and object location within a scene. In terms of object detection, Deep Neural Networks have been recently demonstrated to outperform alterna-tive approaches. The issues related deep learning neural network is its complexity and huge computation, so it is not possible to detect and track the objects in image of high resolution in real time. We proposed scaled YOLOv4 lite model as Single Stage Detector Neural Network for object detection, tracking and it is trained using COCO 2017 dataset. To create the YOLOv4-CSP- P5- P6- P7- P8 networks, the Scaled YOLOv4 applied efficient network scaling strategies. The additional layer in YOLOv4 lite model is added as P8 layer which improves accuracy. Cross-stage-partial (CSP) connections and Mish activation are used in improved network design, such as backbone optimization and Neck (PAN). In the case of YOLOv4, however, it can only be trained once for all resolutions. Width and Height activations have been changed, allowing for faster network training. With YOLOv4 lite model, we used CSPDarkNet-53 model as a backbone. The experimental result show our YOLOv4 lite model can detect and track object up to 28 fps when model run with the video input and has an accuracy of 86.09% when tested on real-time video with resolutions 1920 × 1080 (full HD). AP = 50.81%, AP @50 = 63.6%, and AP @75 = 52.5% for CSPDarkNet-53 model backbone.

Implementation of a motion estimation algorithm for Intel FPGAs using OpenCL

Motion Estimation is one of the main tasks behind any video encoder. It is a computationally costly task; therefore, it is usually delegated to specific or reconfigurable hardware, such as FPGAs. Over the years, multiple FPGA implementations have been developed, mainly using hardware description languages such as Verilog or VHDL. Since programming using hardware description languages is a complex task, it is desirable to use higher-level languages to develop FPGA applications.The aim of this work is to evaluate OpenCL, in terms of expressiveness, as a tool for developing this kind of FPGA applications. To do so, we present and evaluate a parallel implementation of the Block Matching Motion Estimation process using OpenCL for Intel FPGAs, usable and tested on an Intel Stratix 10 FPGA. The implementation efficiently processes Full HD frames completely inside the FPGA. In this work, we show the resource utilization when synthesizing the code on an Intel Stratix 10 FPGA, as well as a performance comparison with multiple CPU implementations with varying levels of optimization and vectorization capabilities. We also compare the proposed OpenCL implementation, in terms of resource utilization and performance, with estimations obtained from an equivalent VHDL implementation.

Smart algorithm in wireless networks for video streaming based on adaptive quantization

SummaryNowadays, smart multimedia network services have become crucial in the healthcare system. The network parameters of Quality of Service (QoS) are widely affecting the efficiency and accuracy of multimedia streaming in wireless environments. This paper proposes an adaptation framework model, which makes a relation between the QP (quantization parameter) in H.264 and H.265 codecs and the QoS of 5G wireless technology. Besides, the effect of QP and packet loss have been studied because of their impact on video streaming. Packet loss of 5G wireless network characteristic is emulated to determine the impact of QP on the received video quality using objective and subjective quality metrics such as PSNR (peak signal to noise ratio), SSIM (structure similarity), and DMOS (differential mean opinion score). In this research, a Testbed is implemented to stream the encoded video from the server to the end users. The application model framework has automatically evaluated the QoE (Quality of Experience). Accordingly, the model detects the defect of network packet loss and selects the optimum QP value to enhance the QoE by the end‐users. The application has been tested on low and high video motions with full high definition (HD) resolution (1920 × 1080) which were taken from ( https://www.xiph.org/downloads/). Test results based on the objective and subjective quality measurements indicate that an optimal QP = 35 and QP = 30 have been chosen for low and high motion respectively to satisfy user QoE requirements.