In the rapidly growing streaming service market, including satellite options, Internet Service Providers (ISPs) face the challenge of continually optimizing network performance to deliver superior video streaming qual...
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ISBN:
(纸本)9798350349405;9798350349399
In the rapidly growing streaming service market, including satellite options, Internet Service Providers (ISPs) face the challenge of continually optimizing network performance to deliver superior video streaming quality, which is vital to optimize customer satisfaction. This pressing need has sparked a drive towards developing advanced Quality of Experience (QoE) prediction models, which are essential in enhancing streaming protocols and guaranteeing smooth viewing experiences for users. However, the efficacy of these models hinges on the availability of extensive, diverse datasets. To fill this critical data void, our study introduces the publicly available LIVE-Viasat real-World Satellite QoE Database, with 179 videos from real-world streaming, encompassing a range of distortions. Enhanced by a study with 54 participants providing detailed QoE feedback, our work not only provides a rich analysis of the determinants of subjective QoE but also delves into how various streaming impairments influence user behavior, thereby offering a more holistic understanding of user satisfaction.
The quality of image and videos plays a vital role in case of real-time systems. images are captured without sufficient illumination, lead to low dynamic range and high propensity for generating high noise levels. The...
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Super resolution (SR) is a technique designed for increasing the spatial resolution in an image from a low resolution (LR) to high resolution (HR) size. SR technology has had a considerable demand in a wide variety of...
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ISBN:
(纸本)9781510673199;9781510673182
Super resolution (SR) is a technique designed for increasing the spatial resolution in an image from a low resolution (LR) to high resolution (HR) size. SR technology has had a considerable demand in a wide variety of applications to recover HR images, such as medicine, engineering, computer vision, pattern recognition and video production, etc. In contrast to interpolation-based algorithms that often introduce distortions or irregular borders, this study proposes an implementation that can preserve the edges and fine details of an original image through the computation of the wavelet decomposition. Different Discrete Wavelet Transform (DWT) families such as: Daubechies, Symlet, and Coiflet were evaluated. The proposed system was implemented on a Raspberry Pi 4 model B, an embedded device, to get around the PC's mobility limitations, making it possible to create an in-expensive and energy- efficient SR system, reducing their complexity in realtime applications. To investigate the visual performance, SR images have been analysed in subjective matter via human perception view, guaranteeing good perception for the images of different nature from three different datasets such as FullHD (DIV2K), medical (Raabin WBC), and remote sensing (Sentinel- 1). The experimental results of designed implementations appear to demonstrate good performance in commonly used objective criteria: execution time, SSIM, and PSNR (0.742 sec., 0.9164, and 38.72 dB), respectively for images with a super resolution size of 1356 x 2040 pixels.
The exponential growth of digital image sharing has amplified concerns regarding data privacy and security, especially for colour images of varying sizes and resolutions. Traditional encryption algorithms often fall s...
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The exponential growth of digital image sharing has amplified concerns regarding data privacy and security, especially for colour images of varying sizes and resolutions. Traditional encryption algorithms often fall short in balancing speed, scalability, and robust security for such diverse image datasets. Addressing this gap, we introduce a novel colour image encryption scheme that synergizes modified Bernoulli map-based random number generation for pixel scrambling with an S-Box-supported diffusion process. Our approach first employs a chaotic random number generator to effectively reorder pixel positions, enhancing confusion. This is followed by a diffusion phase utilizing a robust Khan S-Box to introduce nonlinearity and further obfuscate pixel values. To evaluate the security and efficiency of our method, we conducted extensive tests including differential cryptanalysis using NPCR (Number of Pixel Change Rate) and UACI (Unified Average Changing Intensity) metrics. The results demonstrate that our encryption system exhibits high resistance to differential attacks and achieves superior performance compared to existing methods. By combining fast random number generation with strong S-Box diffusion, our scheme offers a scalable and secure solution for real-time colour image encryption, contributing significant advancements to the field of cryptographic imageprocessing.
Monitoring the movement and actions of humans in video in real-time is an important task. We present a deep learning based algorithm for human action recognition for both RGB and thermal cameras. It is able to detect ...
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ISBN:
(数字)9781510661714
ISBN:
(纸本)9781510661707;9781510661714
Monitoring the movement and actions of humans in video in real-time is an important task. We present a deep learning based algorithm for human action recognition for both RGB and thermal cameras. It is able to detect and track humans and recognize four basic actions (standing, walking, running, lying) in real-time on a notebook with a NVIDIA GPU. For this, it combines state of the art components for object detection (Scaled-YoloV4), optical flow (RAFT) and pose estimation (EvoSkeleton). Qualitative experiments on a set of tunnel videos show that the proposed algorithm works robustly for both RGB and thermal video.
The recent innovations in real-timevideo and image enhancements are allowing much advancement in a wide range of diverse applications. These innovations and advancements provide a new hardware architecture that aims ...
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The recent innovations in real-timevideo and image enhancements are allowing much advancement in a wide range of diverse applications. These innovations and advancements provide a new hardware architecture that aims to improve image visualization, processing speed, and complexity reduction in hardware. The imaging chip concept is introduced in this article to support the Multiprocessing system-on-chip (MPSoC) applications in real-time scenarios on a single chip. The imaging chip model is designed using high-speed interface protocol, which includes different image enhancement algorithms that act as a master model, Advanced Extensible Interface (AXI)-4 as an interface model, and dual-port Memory as a slave model. The image enhancement algorithm includes Brightness control, contrast stretching, Adaptive Median Filtering (AMF), Edge-detection techniques, image Thresholding, and image Histogram. The AXI-4 provides a high-speed interface for communicating master and slave modules. The proposed model works based on the modes of operation to process the enhanced image output in MPSoC. The design supports multiple masters and multiple slave modules with a reconfigurable nature. The imaging chip is a module on the Xilinx ISE environment and implemented on Artix-7 Field-Programmable Gate Array (FPGA), along with the performance metrics like chip Area, time, power, and memory utilization are analyzed with improvements. The model offers low latency and high throughput architecture for real-time Multimedia applications.
video captioning aims to identify multiple objects and their behaviours in a video event and generate captions for the current scene. This task aims to generate a detailed description of the current video in real-time...
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video captioning aims to identify multiple objects and their behaviours in a video event and generate captions for the current scene. This task aims to generate a detailed description of the current video in real-time using natural language, which requires deep learning to analyze and determine the relationships between interesting objects in the frame sequence. In practice, existing methods typically involve detecting objects in the frame sequence and then generating captions based on features extracted through object coverage locations. Therefore, the results of caption generation are highly dependent on the performance of object detection and identification. This work proposes an advanced video captioning approach that works in adaptively and effectively addresses the interdependence between event proposals and captions. Additionally, an attention-based multimodel framework is introduced to capture the main context from the frame and sound in the video scene. Also, an intermediate model is presented to collect the hidden states captured from the input sequence, which performs to extract the main features and implicitly produce multiple event proposals. For caption prediction, the proposed method employs the CARU layer with attention consideration as the primary RNN layer for decoding. Experimental results showed that the proposed work achieves improvements compared to the baseline method and also better performance compared to other state-of-the-art models on the ActivityNet dataset, presenting competitive results in the tasks of video captioning. An advanced video captioning approach is proposed that works in adaptively and effectively addresses the interdependence between event proposals and captions. Additionally, an attention-based multimodel framework is introduced to capture the main context from the frame and sound in the video scene. image
The rapid advancement of network technologies and multimedia applications across various sectors, including military and industry, underscores the importance of safeguarding digital data, especially videos and images....
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The rapid advancement of network technologies and multimedia applications across various sectors, including military and industry, underscores the importance of safeguarding digital data, especially videos and images. Encryption of video is essential in making sensitive content into a completely unrecognizable form. However, traditional methods of encryption face severe difficulties in terms of resolution loss, high computational complexity, and low optimization, which makes the technique less practical and authentic. Addressing such issues, this paper describes the Opposition Lotus Effect-Elliptic Curve Cryptography (OLE-ECC) algorithm. The integration of elliptic curve cryptography with the Opposition Lotus Effect Algorithm enhances video encryption by generating secure key pairs resistant to cryptographic attacks. The video data is encrypted using generated keys by dividing the video stream into segments and applying encryption algorithms to each. This process involves four key phases such as channel segmentation, channel scrambling, generation of key streams from the logistic graph, and channel propagation. The use of multi-equation multi-key cryptography increases the safety of video data significantly while encrypted because it involves applying a number of mathematical equations along with multiple keys. This technique effectively manages the encryption of dynamically generated video files and allows the possibility of encrypting different segments of the video with different keys, making it efficient in performance for real-time streaming applications. For video decryption, the encrypted video is sent over the communication channel, the decryption process includes bit-wise exclusion, rearrangement of channel blocks, and pixel reordering, which increases the reliability of the recovered video frames. Furthermore, the program for revealing, combined with XOR operations, allows the recovery of hidden pixels without loss of quality. Message Authorization Codes a
As it is a pre-processing task, estimation of video-sequence-based homography requires low computational costs and fast evaluation. However, current algorithms for video sequence tasks are commonly based on image-pair...
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As it is a pre-processing task, estimation of video-sequence-based homography requires low computational costs and fast evaluation. However, current algorithms for video sequence tasks are commonly based on image-pair homography and do not consider the inner properties of the video sequences. Therefore they take unnecessary computational resources. In this work, we propose a novel algorithm with a first-order estimation method to fill the gap between estimation of image-pairs and video sequence homography. By considering the continuous movement of the camera, the proposed algorithm adopts a first-order estimation to accelerate the estimation process while maintaining its robustness. Instead of extracting many image features from every frame, we demonstrate that estimating a homography matrix with pixel-based texture patterns is effective and sufficient for video sequences. Experiments show that homography estimation with simple one-dimensional texture vectors, as used in our algorithm, can surpass state-of-the-art feature-based algorithms and deep-learning-based methods. This first-order estimation method was more than 40 times faster and real-time estimation used only the CPU.
The deep integration of new-generation information technology and manufacturing is triggering far-reaching industrial changes. Machine vision inspection is widely used in large-scale repetitive industrial production p...
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