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Статті в журналах з теми "Video compression"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 25 липня 2025

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1

Rajasekhar, H., and B. Prabhakara Rao. "An Efficient Video Compression Technique Using Watershed Algorithm and JPEG-LS Encoding." Journal of Computational and Theoretical Nanoscience 13, no. 10 (2016): 6671–79. http://dx.doi.org/10.1166/jctn.2016.5613.

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Анотація:

In the previous video compression method, the videos were segmented by using the novel motion estimation algorithm with aid of watershed method. But, the compression ratio (CR) of compression with novel motion estimation algorithm was not giving an adequate result. Moreover this methods performance is needed to be improved in the encoding and decoding processes. Because most of the video compression methods have utilized encoding techniques like JPEG, Run Length, Huffman coding and LSK encoding. The improvement of the encoding techniques in the compression process will improve the compression

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2

Veeraswamy, Mr D. "3D-Based Compression Framework for High Quality Video Streaming." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2628–36. https://doi.org/10.22214/ijraset.2025.68746.

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Анотація:

Video compression plays a pivotal role in managing the storage and transmission of multimedia content, especially in bandwidth-constrained environments. Nowadays, volumetric video has emerged as an attractive multimedia application, which provides highly immersive watching experiences. How- ever, streaming the volumetric video demands prohibitively high bandwidth. Thus, effectively compressing its underlying point cloud frames is essential to deploying the volumetric videos. The existing compression techniques are either 3D-based or 2D-based, but they still have drawbacks when being deployed i

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3

Mishra, Amit Kumar. "Versatile Video Coding (VVC) Standard: Overview and Applications." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, no. 2 (2019): 975–81. http://dx.doi.org/10.17762/turcomat.v10i2.13578.

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Information security includes picture and video compression and encryption since compressed data is more secure than uncompressed imagery. Another point is that handling data of smaller sizes is simple. Therefore, efficient, secure, and simple data transport methods are created through effective data compression technology. Consequently, there are two different sorts of compression algorithm techniques: lossy compressions and lossless compressions. Any type of data format, including text, audio, video, and picture files, may leverage these technologies. In this procedure, the Least Significant

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4

Et. al., G. Megala,. "State-Of-The-Art In Video Processing: Compression, Optimization And Retrieval." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (2021): 1256–72. http://dx.doi.org/10.17762/turcomat.v12i5.1793.

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Анотація:

Video compression plays a vital role in the modern social media networking with plethora of multimedia applications. It empowers transmission medium to competently transfer videos and enable resources to store the video efficiently. Nowadays high-resolution video data are transferred through the communication channel having high bit rate in order to send multiple compressed videos. There are many advances in transmission ability, efficient storage ways of these compressed video where compression is the primary task involved in multimedia services. This paper summarizes the compression standard

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5

Strachan, David, Margarida DeBruin, and Robert Marhong. "Video Compression." SMPTE Journal 105, no. 2 (1996): 68–73. http://dx.doi.org/10.5594/j04666.

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6

Butar-Butar, Ronaldo P. "Kompresi File Video Menggunakan Algoritma Yamamoto’s Recursive Code." Bulletin of Data Science 3, no. 1 (2023): 149–59. https://doi.org/10.47065/bulletinds.v3i1.5693.

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Анотація:

At this time it is very common to use it in all aspects, from advertising, films, even learning during the COVID-19 pandemic using video media as a learning medium. Large video file sizes can reduce free space on storage media such as hard drives, flash drives and so on. The more videos that are stored, the less free space is left, or even no free space is left on the storage media. This will not cause a problem if the storage media used has a large capacity, but not everyone has a large storage media. One way to overcome this problem is to use compression techniques. Data compression is a pro

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7

Gao, Lisha, Zhoujun Ma, Shuo Han, Tiancheng Zhao, Qingcheng Liu, and Zhangjie Fu. "Robust Mixed-Rate Region-of-Interest-Aware Video Compressive Sensing for Transmission Line Surveillance Video." Information 15, no. 9 (2024): 555. http://dx.doi.org/10.3390/info15090555.

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Анотація:

Classic video compression methods usually suffer from long encode time and requires large memories, making it hard to deploy on edge devices; thus, video compressive sensing, which requires less resources during encoding, is receiving more attention. We propose a robust mixed-rate ROI-aware video compressive sensing algorithm for transmission line surveillance video compression. The proposed method compresses foreground targets and background frames separately and uses reversible neural network to reconstruct original frames. The result on transmission line surveillance video data shows that t

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8

Mohammed, Dhrgham Hani, and Laith Ali Abdul-Rahaim. "A Proposed of Multimedia Compression System Using Three - Dimensional Transformation." Webology 18, SI05 (2021): 816–31. http://dx.doi.org/10.14704/web/v18si05/web18264.

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Анотація:

Video compression has become especially important nowadays with the increase of data transmitted over transmission channels, the reducing the size of the videos must be done without affecting the quality of the video. This process is done by cutting the video thread into frames of specific lengths and converting them into a three-dimensional matrix. The proposed compression scheme uses the traditional red-green-blue color space representation and applies a three-dimensional discrete Fourier transform (3D-DFT) or three-dimensional discrete wavelet transform (3D-DWT) to the signal matrix after c

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9

P, Srividya. "Optimization of Lossless Compression Algorithms using Multithreading." Journal of Information Technology and Sciences 9, no. 1 (2023): 36–42. http://dx.doi.org/10.46610/joits.2022.v09i01.005.

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Анотація:

The process of reducing the number of bits required to characterize data is referred to as compression. The advantages of compression include a reduction in the time taken to transfer data from one point to another, and a reduction in the cost required for the storage space and network bandwidth. There are two types of compression algorithms namely lossy compression algorithm and lossless compression algorithm. Lossy algorithms find utility in compressing audio and video signals whereas lossless algorithms are used in compressing text messages. The advent of the internet and its worldwide usag

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10

P, Srividya. "Optimization of Lossless Compression Algorithms using Multithreading." Journal of Information Technology and Sciences 9, no. 1 (2023): 36–42. http://dx.doi.org/10.46610/joits.2023.v09i01.005.

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Анотація:

The process of reducing the number of bits required to characterize data is referred to as compression. The advantages of compression include a reduction in the time taken to transfer data from one point to another, and a reduction in the cost required for the storage space and network bandwidth. There are two types of compression algorithms namely lossy compression algorithm and lossless compression algorithm. Lossy algorithms find utility in compressing audio and video signals whereas lossless algorithms are used in compressing text messages. The advent of the internet and its worldwide usag

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11

Khan, Umair, Sajjad Afrakhteh, Federico Mento, et al. "Coronavirus disease 2019 patients prognostic stratification based on low complex lung ultrasound video compression." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A189. http://dx.doi.org/10.1121/10.0018617.

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Анотація:

In the last years, efforts have been made towards automating semi-quantitative analysis of lung ultrasound (LUS) data. To this end, several methods have been proposed with a focus on frame-level classification. However, no extensive work has been done to evaluate LUS data directly at the video level. This study proposes an effective video compression and classification technique for assessing LUS data. This technique is based on maximum, mean, and minimum intensity projection (with respect to the temporal dimension) of LUS video data. This compression allows preserving hyper- and hypo-echoic r

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12

Laghari, Asif Ali, Hui He, Shahid Karim, Himat Ali Shah, and Nabin Kumar Karn. "Quality of Experience Assessment of Video Quality in Social Clouds." Wireless Communications and Mobile Computing 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8313942.

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Анотація:

Video sharing on social clouds is popular among the users around the world. High-Definition (HD) videos have big file size so the storing in cloud storage and streaming of videos with high quality from cloud to the client are a big problem for service providers. Social clouds compress the videos to save storage and stream over slow networks to provide quality of service (QoS). Compression of video decreases the quality compared to original video and parameters are changed during the online play as well as after download. Degradation of video quality due to compression decreases the quality of

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13

Lu, Ming, Zhihao Duan, Fengqing Zhu, and Zhan Ma. "Deep Hierarchical Video Compression." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 8859–67. http://dx.doi.org/10.1609/aaai.v38i8.28733.

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Анотація:

Recently, probabilistic predictive coding that directly models the conditional distribution of latent features across successive frames for temporal redundancy removal has yielded promising results. Existing methods using a single-scale Variational AutoEncoder (VAE) must devise complex networks for conditional probability estimation in latent space, neglecting multiscale characteristics of video frames. Instead, this work proposes hierarchical probabilistic predictive coding, for which hierarchal VAEs are carefully designed to characterize multiscale latent features as a family of flexible pri

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14

Jeon, Minseong, and Kyungjoo Cheoi. "Efficient Video Compression Using Afterimage Representation." Sensors 24, no. 22 (2024): 7398. http://dx.doi.org/10.3390/s24227398.

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Анотація:

Recent advancements in large-scale video data have highlighted the growing need for efficient data compression techniques to enhance video processing performance. In this paper, we propose an afterimage-based video compression method that significantly reduces video data volume while maintaining analytical performance. The proposed approach utilizes optical flow to adaptively select the number of keyframes based on scene complexity, optimizing compression efficiency. Additionally, object movement masks extracted from keyframes are accumulated over time using alpha blending to generate the fina

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15

Patil, Shivganga, and Lakshmi Patil. "DESIGN AND ANALYSIS ON IMAGE COMPRESSION USING NEURAL NETWORKS." ICTACT Journal on Image and Video Processing 14, no. 4 (2024): 3301–4. http://dx.doi.org/10.21917/ijivp.2024.0469.

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Анотація:

Due to the explosion of video based information proliferating in the world due to the ubiquitous usage of video cameras the amount of video based information that is currently being generated around the world is huge. And due to security purposes it is becoming imperative that these video data needs to be stored in computer memory for an extended period of time for referrals by security agencies. Because of the advancement of imaging technologies that is being used nowadays it is possible to capture extremely detailed high definition images. But it is not physically possible to store all these

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16

Flierl, Markus, and Bernd Girod. "Multiview Video Compression." IEEE Signal Processing Magazine 24, no. 99 (2007): 66–76. http://dx.doi.org/10.1109/msp.2007.4317465.

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17

Flierl, Markus, and Bernd Girod. "Multiview Video Compression." IEEE Signal Processing Magazine 24, no. 6 (2007): 66–76. http://dx.doi.org/10.1109/msp.2007.905699.

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18

Im, Byungwook, Seungho Baek, Kinam Jun, Dokyoung Kim, Juhyun Jung, and Daesik Kim. "A Study on the Video Compression Pre-processing Method for Video Transmission and Target Detection in Ultra-narrowband Environment." Journal of the Korea Institute of Military Science and Technology 23, no. 1 (2020): 28–36. https://doi.org/10.9766/kimst.2020.23.1.028.

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Анотація:

Due to the continued demand for high-definition video, video compression technology is steadily developing and the High Efficiency Video Coding standard was established in 2013. However, despite the development of this compression technology, it is very difficult to smoothly transmit VGA-level videos in Ultra-narrowband environments. In this paper, the target information preprocessing algorithm is presented for smooth transmission of target images moving in forest or open-terrain in Ultra-narrowband environment. In addition, for algorithm verification, the target information preprocessing algo

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19

Leong, Chi Wa, Behnoosh Hariri, and Shervin Shirmohammadi. "Exploiting Orientational Redundancy in Multiview Video Compression." International Journal of Computer and Electrical Engineering 7, no. 2 (2015): 70–81. http://dx.doi.org/10.17706/ijcee.2015.v7.873.

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20

Miss., Kirti Lakapate*1 Prof. Mahesh Karanjkar2 &. Prof. Namdev Pergad3. "ADAPTIVE VIDEO COMPRESSION AND TRANSMISSION OVER HETEROGENEOUS NETWORKS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 4 (2018): 156–63. https://doi.org/10.5281/zenodo.1215430.

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Анотація:

Video may go through different sorts of heterogeneous systems amid the procedure of transmission, which impacts affects the continuous video quality. The previous technique focus on how to compress videos based on the video flow without considering the real-time network information. This system presents an adaptive method that combines video transmission control and video encoding the system over heterogeneous networks. This system includes steps such as : first, to collect and standardize the real-time information describing the network and the video, then to assess the video quality and calc

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21

Lu, Hongrui, Yingjun Zhang, and Zhuolin Wang. "Time Delay Optimization of Compressing Shipborne Vision Sensor Video Based on Deep Learning." Journal of Marine Science and Engineering 11, no. 1 (2023): 122. http://dx.doi.org/10.3390/jmse11010122.

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Анотація:

As the technology for offshore wireless transmission and collaborative innovation in unmanned ships continues to mature, research has been gradually carried out in various countries on methods of compressing and transmitting perceptual video while driving ships remotely. High Efficiency Video Coding (H.265/HEVC) has played an extremely important role in the field of Unmanned Aerial Vehicle (UAV) and autopilot, and as one of the most advanced coding schemes, its performance in compressing visual sensor video is excellent. According to the characteristics of shipborne vision sensor video (SVSV),

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22

Dewi, Siagian. "Implementasi Algoritma Elias Gamma Code Untuk Kompresi File Video Pada Aplikasi Drama Korea." Jurnal Sains dan Teknologi Informasi 1, no. 3 (2022): 90–95. http://dx.doi.org/10.47065/jussi.v1i3.2183.

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Анотація:

Korean drama application is an application that is useful as a means of entertainment. When the user downloads the Korean drama application video on a smartphone (cellphone) and saves the video file downloaded by the Korean drama, it will produce a large file size. Because in general we do the activity of downloading many Korean drama videos, we get a long video duration. The longer the video duration, the larger the file size will be. Then the large file size will cause problems because it is limited by storage capacity. When you want to send it, it will take a long time to solve this problem

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23

Hadizadeh, Hadi, and Ivan V. Bajic. "Saliency-Aware Video Compression." IEEE Transactions on Image Processing 23, no. 1 (2014): 19–33. http://dx.doi.org/10.1109/tip.2013.2282897.

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24

Cramer, C., E. Gelenbe, and P. Gelenbe. "Image and video compression." IEEE Potentials 17, no. 1 (1998): 29–33. http://dx.doi.org/10.1109/45.652854.

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25

Tudor, P. N. "MPEG-2 video compression." Electronics & Communication Engineering Journal 7, no. 6 (1995): 257–64. http://dx.doi.org/10.1049/ecej:19950606.

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26

Chen, Zhibo, Tianyu He, Xin Jin, and Feng Wu. "Learning for Video Compression." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 2 (2020): 566–76. http://dx.doi.org/10.1109/tcsvt.2019.2892608.

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27

Acharjee, Suvojit, Sayan Chakraborty, Wahiba Ben Abdessalem Karaa, Ahmad Taher Azar, and Nilanjan Dey. "Performance Evaluation of Different Cost Functions in Motion Vector Estimation." International Journal of Service Science, Management, Engineering, and Technology 5, no. 1 (2014): 45–65. http://dx.doi.org/10.4018/ijssmet.2014010103.

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Анотація:

Video is an important medium in terms of information sharing in this present era. The tremendous growth of video use can be seen in the traditional multimedia application as well as in many other applications like medical videos, surveillance video etc. Raw video data is usually large in size, which demands for video compression. In different video compressing schemes, motion vector is a very important step to remove the temporal redundancy. A frame is first divided into small blocks and then motion vector for each block is computed. The difference between two blocks is evaluated by different

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28

Nevart A. Minas and Faten H. Al-Qadhee. "Digital Video Compression Using DCT-Based Iterated Function System (IFS)." Tikrit Journal of Pure Science 22, no. 6 (2023): 125–30. http://dx.doi.org/10.25130/tjps.v22i6.800.

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Large video files processing involves a huge volume of data. The codec, storage systems and network needs resource utilization, so it becomes important to minimize the used memory space and time to distribute these videos over the Internet using compression techniques. Fractal image and video compression falls under the category of lossy compression. It gives best results when used for natural images. This paper presents an efficient method to compress an AVI (Audio Video Interleaved) file with fractal video compression(FVC). The video first is separated into a sequence of frames that ar

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29

Chutke, Sravanthi ,., N. M. Nandhitha, and Praveen Kumar. L. "Accordion-based SPIHT Coding for Video Compression using DCT/DWT." WSEAS TRANSACTIONS ON CIRCUITS AND SYSTEMS 24 (February 19, 2025): 24–30. https://doi.org/10.37394/23201.2025.24.3.

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Video signals tend to contain significant temporal compression because of the high degree of correlation between consecutive frames. This means that existing video compression technologies have not utilized all the information that is readily available in such signals. In particular, we will discuss a novel video compression technique that is capable of exploiting temporal redundancy to improve the details while processing requirements are kept low. In our case, the transformation is applied by converting the 3D video data into 2D space. This transformation allows for applying 2D transforms to

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30

P, Madhavee Latha, and Annis Fathima A. "REVIEW ON IMAGE AND VIDEO COMPRESSION STANDARDS." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (2017): 373. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19760.

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Анотація:

Nowadays, the number of photos taken each day is growing exponentially on phones and the number of photos uploading on Internet is also increasing rapidly. This explosion of photos in Internet and personal devices such as phones posed a challenge to the effective storage and transmission.Multimedia files are the files having text, images, audio, video, and animations, which are large and require lots of hard disk space. Hence, these files take more time to move from one place to another place over the Internet. Image compression is an effective way to reduce the storage space and speedup the t

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31

Tang, Chuanbo, Xihua Sheng, Zhuoyuan Li, Haotian Zhang, Li Li, and Dong Liu. "Offline and Online Optical Flow Enhancement for Deep Video Compression." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (2024): 5118–26. http://dx.doi.org/10.1609/aaai.v38i6.28317.

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Анотація:

Video compression relies heavily on exploiting the temporal redundancy between video frames, which is usually achieved by estimating and using the motion information. The motion information is represented as optical flows in most of the existing deep video compression networks. Indeed, these networks often adopt pre-trained optical flow estimation networks for motion estimation. The optical flows, however, may be less suitable for video compression due to the following two factors. First, the optical flow estimation networks were trained to perform inter-frame prediction as accurately as possi

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32

Yang, Yixin, Zhiqang Xiang, and Jianbo Li. "Research on Low Frame Rate Video Compression Algorithm in the Context of New Media." Security and Communication Networks 2021 (September 27, 2021): 1–10. http://dx.doi.org/10.1155/2021/7494750.

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Анотація:

When using the current method to compress the low frame rate video animation video, there is no frame rate compensation for the video image, which cannot eliminate the artifacts generated in the compression process, resulting in low definition, poor quality, and low compression efficiency of the compressed low frame rate video animation video. In the context of new media, the linear function model is introduced to study the frame rate video animation video compression algorithm. In this paper, an adaptive detachable convolutional network is used to estimate the offset of low frame rate video a

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33

Mochurad, Lesia. "A Comparison of Machine Learning-Based and Conventional Technologies for Video Compression." Technologies 12, no. 4 (2024): 52. http://dx.doi.org/10.3390/technologies12040052.

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Анотація:

The growing demand for high-quality video transmission over bandwidth-constrained networks and the increasing availability of video content have led to the need for efficient storage and distribution of large video files. To improve the latter, this article offers a comparison of six video compression methods without loss of quality. Particularly, H.255, VP9, AV1, convolutional neural network (CNN), recurrent neural network (RNN), and deep autoencoder (DAE). The proposed decision is to use a dataset of high-quality videos to implement and compare the performance of classical compression algori

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34

Saputra, Indra, Harun Mukhtar, and Januar Al Amien. "Analisis Perbandingan Performa Codec H.264 & H.265 Video Streaming Dari Segi Quality of Service." Jurnal CoSciTech (Computer Science and Information Technology) 2, no. 1 (2021): 9–13. http://dx.doi.org/10.37859/coscitech.v2i1.2190.

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Анотація:

Video streaming is a technology that is often used when watching videos on the internet without having to download then video to play it. Some problems that affect the performance of video streaming are such as the large capacity of the video size, the capacity of the video size affects the smoothness when streaming video. In this research, two video compression methods will be compared, namely H.264 codec and H.265 codec. The test is carried out to determine the effect of using the compression method according to changes in the codec ang framerate used. After testing the two codec, it can be

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35

Arjun, Mantri, Kathiriya Satish, and S. Yadav Purshotam. "Optimizing Video Encoding and Streaming Quality on Social Media Platforms." Journal of Scientific and Engineering Research 9, no. 12 (2022): 177–81. https://doi.org/10.5281/zenodo.13348093.

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Анотація:

In the era of digital communication, social media platforms have become essential for sharing video content. To meet user expectations for high-quality video without buffering or lag, these platforms employ sophisticated video encoding and streaming techniques. This paper examines methods for optimizing video encoding and streaming quality, focusing on adaptive bitrate streaming (ABR) and advanced video compression algorithms. ABR adjusts video quality in real-time based on network conditions, while compression algorithms like H.264, H.265 (HEVC), and VP9 reduce file sizes without compromising

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36

Dhungel, Prasanga, Prashant Tandan, Sandesh Bhusal, Sobit Neupane, and Subarna Shakya. "Video Compression for Surveillance Application using Deep Neural Network." June 2020 2, no. 2 (2020): 131–45. http://dx.doi.org/10.36548/jaicn.2020.2.006.

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Анотація:

We present a new approach to video compression for video surveillance by refining the shortcomings of conventional approach and substitute each traditional component with their neural network counterpart. Our proposed work consists of motion estimation, compression and compensation and residue compression, learned end-to-end to minimize the rate-distortion trade off. The whole model is jointly optimized using a single loss function. Our work is based on a standard method to exploit the spatio-temporal redundancy in video frames to reduce the bit rate along with the minimization of distortions

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Lau, Tiffany Wai Shan, Anthony Robert Lim, Kyra Anne Len, and Loren Gene Yamamoto. "Chest compression efficacy of child resuscitators." Journal of Paramedic Practice 13, no. 11 (2021): 448–55. http://dx.doi.org/10.12968/jpar.2021.13.11.448.

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Background: Chest compression efficacy determines blood flow in cardiopulmonary resuscitation (CPR) and relies on body mechanics, so resuscitator weight matters. Individuals of insufficient weight are incapable of generating a sufficient downward chest compression force using traditional methods. Aims: This study investigated how a resuscitator's weight affects chest compression efficacy, determined the minimum weight required to perform chest compressions and, for children and adults below this minimum weight, examine alternate means to perform chest compressions. Methods: Volunteers aged 8 y

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Shen, Yang, Jinqin Lu, Li Zhu, and Fangming Deng. "Research on Deep Compression Method of Expressway Video Based on Content Value." Electronics 11, no. 23 (2022): 4024. http://dx.doi.org/10.3390/electronics11234024.

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Анотація:

Aiming at the problem that the storage space and network bandwidth of expressway surveillance video are occupied largely due to data redundancy and sparse information, this paper proposes a deep compression method of expressway video depth based on content value. Firstly, the YOLOv4 algorithm is used to analyze the content value of the original video, extract video frames with vehicle information, and eliminate unintentional frames. An improved CNN is then designed by adding Feature Pyramids and the Inception module to accelerate the extraction and fusion of features at all levels and improve

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39

A., D. Senthil Kumar, S. Anandhi T., and Muthu Ranganath. "PIXEL INTEGRATION TECHNIQUE FOR MULTI-IMAGE AND VIDEO COMPRESSION FOR DATA SECURITY." International Journal of Current Research and Modern Education 3, no. 1 (2018): 31–40. https://doi.org/10.5281/zenodo.1145484.

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This paper approaches security application for digital image and video processing. The techniques involve JPEG image and H.264 video compression, Simon cryptography algorithm using Verilog HDL followed by Image Interleaving, and last by Pixel Integration to generate integrated multi-image and video. The user can choose any of the image/ videos among the several integrated inputs displayed with a unique security. With the secure key assigned for each input, the original image or video is displayed by decrypting it from multiple image and videos.

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40

Chiman, Kwan, Larkin Jude, Budavari Bence, Shang Eric, and D. Tran Trac. "Perceptually Lossless Compression with Error Concealment for Periscope and Sonar Videos." Signal & Image Processing: An International Journal (SIPIJ) 10, February (2019): 1–14. https://doi.org/10.5281/zenodo.3187693.

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We present a video compression framework that has two key features. First, we aim at achieving perceptually lossless compression for low frame rate videos (6 fps). Four well-known video codecs in the literature have been evaluated and the performance was assessed using four well-known performance metrics. Second, we investigated the impact of error concealment algorithms for handling corrupted pixels due to transmission errors in communication channels. Extensive experiments using actual videos have been performed to demonstrate the proposed framework

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41

Kwon, Ilhwan, Jun Li, and Mukesh Prasad. "Lightweight Video Super-Resolution for Compressed Video." Electronics 12, no. 3 (2023): 660. http://dx.doi.org/10.3390/electronics12030660.

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Video compression technology for Ultra-High Definition (UHD) and 8K UHD video has been established and is being widely adopted by major broadcasting companies and video content providers, allowing them to produce high-quality videos that meet the demands of today’s consumers. However, high-resolution video content broadcasting is not an easy problem to be resolved in the near future due to limited resources in network bandwidth and data storage. An alternative solution to overcome the challenges of broadcasting high-resolution video content is to downsample UHD or 8K video at the transmission

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42

Sharabayko, Maxim P., and Nikolay G. Markov. "Fast Search for Intra Prediction Mode in H.265/HEVC Video Compression." Key Engineering Materials 685 (February 2016): 897–901. http://dx.doi.org/10.4028/www.scientific.net/kem.685.897.

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Mechanical engineering, chemical engineering and other industries have a high demand for the video compression systems that are used, e.g., in CCTV and video sensing. The newest video compression standard H.265/HEVC provides the compression rate of 100–300 times to the uncompressed video. The side effect is the increase of a computational complexity of the compression system. This high complexity obstructs the industrial implementation of H.265/HEVC video compression systems. One of the main objectives is to reduce intra compression complexity. In this paper, we present our algorithm of a fast

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43

Basha, Sardar N., and A. Rajesh. "Scalable Video Coding Using Accordion Discrete Wavelet Transform and Tucker Decomposition for Multimedia Applications." Journal of Computational and Theoretical Nanoscience 16, no. 2 (2019): 601–8. http://dx.doi.org/10.1166/jctn.2019.7777.

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The digital world demands the transmission and storage of high quality video for streaming and broadcasting applications, the constraints are the network bandwidth and the memory of devices for the various multimedia and scientific applications, the video consists of spatial and temporal redundancies. The objective of any video compression algorithm is to eliminate the redundant information from the video signal during compression for effective transmission and storage. The correlation between the successive frames has not been exploited enough by the current compression algorithms. In this pa

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44

Noor, Noor, and Qusay Abboodi Ali. "A New Method for Intelligent Multimedia Compression Based on Discrete Hartley Matrix." Fusion: Practice and Applications 16, no. 2 (2024): 108–17. http://dx.doi.org/10.54216/fpa.160207.

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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 relativel

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45

Makiyah, Estabraq, and Nassr N. Khamees. "VOXEL Video Streaming Over Wireless Networks." Iraqi Journal of Information and Communication Technology 7, no. 2 (2024): 1–13. http://dx.doi.org/10.31987/ijict.7.2.244.

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High-density point clouds expressing attractive 3D images are attracting attention. These gigantic media require large bandwidth allocations, making them problematic to stream to resource-constrained hand-held devices. This paper proposes a method for point cloud compression and streaming of large point clouds using a web server. Storing large point cloud videos on a web server allows users to publish data sets without using additional applications or sending large amounts of data ahead of time. HTTP/2 improves transfer efficiency by compressing headers into binary format and reduces latency b

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46

Putra, Arief Bramanto Wicaksono, Rheo Malani, Bedi Suprapty, Achmad Fanany Onnilita Gaffar, and Roman Voliansky. "Inter-Frame Video Compression based on Adaptive Fuzzy Inference System Compression of Multiple Frame Characteristics." Knowledge Engineering and Data Science 6, no. 1 (2023): 1. http://dx.doi.org/10.17977/um018v6i12023p1-14.

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Video compression is used for storage or bandwidth efficiency in clip video information. Video compression involves encoders and decoders. Video compression uses intra-frame, inter-frame, and block-based methods. Video compression compresses nearby frame pairs into one compressed frame using inter-frame compression. This study defines odd and even neighboring frame pairings. Motion estimation, compensation, and frame difference underpin video compression methods. In this study, adaptive FIS (Fuzzy Inference System) compresses and decompresses each odd-even frame pair. First, adaptive FIS train

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Reuss, Edward. "VC-5 Video Compression for Mezzanine Compression Workflows." SMPTE Motion Imaging Journal 124, no. 1 (2015): 55–61. http://dx.doi.org/10.5594/j18500.

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Malik, Manas. "Framework For Lossless Data Compression Using Python." International Journal of Engineering and Computer Science 8, no. 03 (2019): 24575–85. http://dx.doi.org/10.18535/ijecs/v8i03.4296.

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Анотація:

A lot has been done in the field of data compression, yet we don’t have a proper application for compressing daily usage files. There are appropriate and very specific tools online that provide files to be compressed and saved, but the content we use for streaming our videos, be it a Netflix video or a gaming theater play, data consumed is beyond the calculation of a user. Back-end developers know all about it and as developers we have acknowledged it but not yet achieved it in providing on an ease level. Since the user would not never be concerned about compression, developers can always take

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49

Zhu, Ye. "An investigation of machine learning-based video compression techniques." Applied and Computational Engineering 47, no. 1 (2024): 23–27. http://dx.doi.org/10.54254/2755-2721/47/20241113.

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As video technology continues to seamlessly weave itself into the fabric of daily life, there is a growing need for enhanced storage and efficient video transmission. This surge in demand has led to heightened expectations and standards for video compression technology. Machine learning as an up-and-coming technology can play its advantages in the field of video compression. This article reviews the current state of research on combining video compression techniques with machine learning. The article provides an overview of various research avenues for enhancement, spanning from conventional v

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Liu, Shangdong, Puming Cao, Yujian Feng, et al. "NRVC: Neural Representation for Video Compression with Implicit Multiscale Fusion Network." Entropy 25, no. 8 (2023): 1167. http://dx.doi.org/10.3390/e25081167.

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Анотація:

Recently, end-to-end deep models for video compression have made steady advancements. However, this resulted in a lengthy and complex pipeline containing numerous redundant parameters. The video compression approaches based on implicit neural representation (INR) allow videos to be directly represented as a function approximated by a neural network, resulting in a more lightweight model, whereas the singularity of the feature extraction pipeline limits the network’s ability to fit the mapping function for video frames. Hence, we propose a neural representation approach for video compression wi

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